reported speech for class 10

English Grammar
Grammar Exercises
Reported Speech Exercises For Class 10

Reported Speech Exercises with Answers for Class 10

One of the English grammar concepts that almost all of us would have studied in our junior classes is reported speech . Having a clear understanding of reported speech helps students use sentences correctly. This article provides reported speech exercises for class 10 students.

Reported Speech Exercises for Class 10 with Answers

Here is an exercise on the transformation of direct speech to indirect speech. Go through the following sentences, work them out and then check your answers to assess how far you have understood their usage.

Change as directed

Read the following sentences and change them into reported speech.

Mimi said, “I have been writing this letter.”
I said, “Sam’s driving the car.”
My uncle said, “I am cooking lunch.”
My brother said, “I had already eaten.”
The old lady said to the girl, “Where do you come from?”
Jon said, “I like to play rugby.”
My mother said, “I get up early every morning.”
The maths teacher said, “Three divided by three is one.”
Mohit said, “Switzerland is a very beautiful country.”
Ruben said, “It is very cold outside.”
The teacher said, “The French Revolution took place in 1789.”
Uma said, “I saw a Royal Bengal Tiger in the zoo.”
Luke said, “I can do this homework.”
Aswini said to her mother, “I have passed the test”.
Daphne said to Antony, “I will go to London tomorrow.”
The boy said, “My father is sleeping.”
The traffic police said to us, “Where are you going?”
The man shouted, “Let me go.”
Shivina said, “Alas! I am lost.”
“I know her contact number,” said Helena.
Stefen said, “My granny is making pasta.”
Raj said to Simran, “Have you ever been to the National Museum?”
Anish said to Sid, “Please lend me the book.”
The teacher said to the parents, “Shelly is working very hard.”
Joshua said, “I have completed my assignment.”
I said to Alka, “How long will you stay here?”
The child told his dad, “I want an ice cream.”
Meera said, “I am not feeling well.”
The teacher said to Vivek, “Draw the diagram of the plant’s parts.”
Irin said, “I am playing the piano.”
My mother said to me, “Help me carry this bag.”
Rahul said, “My sister is very helpful.”
The news reporter said, “The flight will be delayed by a few hours due to heavy rains.”
Urmi said to her mother, “I want a slice of pizza.”
I said to Daniel, “Are you reading this book?”
Mimi said that she had been writing that letter.
I said that Sam was driving the car.
My uncle said that he was cooking lunch.
My brother said that he had already eaten.
The old lady asked the girl where she came from.
Jon said that he likes to play rugby.
My mother said that she gets up early every morning.
The maths teacher said that three divided by three is one.
Mohit said that Switzerland was a very beautiful country.
Ruben said that it was very cold outside.
The teacher said that the French Revolution took place in 1789.
Uma said that she saw a Royal Bengal Tiger in the zoo.
Luke said that he could do that homework.
Aswini told her mother that she had passed the test.
Daphne informed Antony that she would go to London the next day.
The boy said that his father was sleeping.
The traffic police asked us where we were going.
The man shouted to them to let him go.
Shivina exclaimed sadly that she was lost.
Helena said that she knew her contact number.
Stefen said that his granny was making pasta.
Raj asked Simran if she had ever been to the National Museum.
Anish requested Sid to lend him the book.
The teacher told the parents that Shelly was working very hard.
Joshua said that he had completed his assignment.
I asked Alka how long she would stay there.
The child told his dad that he wants an ice cream.
Meera said that she was not feeling well.
The teacher instructed Vivek to draw the diagram of the plant’s parts.
Irin said that she was playing the piano.
My mother asked me to help her carry the bag.
Rahul said that his sister was very helpful.
The news reporter said that the flight would be delayed by a few hours due to heavy rains.
Urmi said to her mother that she wanted a slice of pizza.
I asked Daniel if he was reading that book.

Frequently Asked Questions

What is direct narration.

When the actual words/sentences spoken by the speaker are quoted in a speech, it is known as direct speech/narration.

Is knowing reported speech necessary for Class 10?

Having a basic understanding of reported speech is necessary for students of any class or age. Solving exercises on direct and indirect speech will help them understand thoroughly and use them correctly.

What is indirect speech?

When the quoted speech is reported in the form of a narrative without changing the meaning of the actual quotation/words by the speaker, it is called indirect speech. Indirect speech is also known as reported speech.

Register with BYJU'S & Download Free PDFs

25,000+ students realised their study abroad dream with us. Take the first step today

Meet top uk universities from the comfort of your home, here’s your new year gift, one app for all your, study abroad needs, start your journey, track your progress, grow with the community and so much more.

Verification Code

An OTP has been sent to your registered mobile no. Please verify

Thanks for your comment !

Our team will review it before it's shown to our readers.

Reported Speech /

Reported Speech For Class 10: Exciting Exercises with Answers [PDF]

Updated on
Jan 12, 2024

Reported speech plays an integral role in maintaining effective communication skills . It also ensures accuracy, objectivity, and clarity among the speakers. Reported Speech is an essential linguistic tool from everyday conversations to formal writing. It is important to teach reported speech to Class 10 to give them a wider scope of the English Language and vocabulary . Reported Speech is effective in conveying the thoughts and ideas of others accurately and without causing any misrepresentation.

This Blog Includes:

What is reported speech in english grammar, reported speech for class 10 exercise 1 – mcqs, exercise 2 – change the sentences from direct to indirect speech.

Reported Speech is often called Indirect Speech, which is not the exact words spoken by the speaker and is not written inside the quotation marks. It is the representation of the words spoken by the speaker in the past by another person. Reported Speech involves transforming verb tenses, pronouns, and sometimes other elements. The changes are important to accurately represent the reported information while integrating it into the speaker’s sentence structure.

Must Read! Reported Speech: Definition, Rules, Usage with Examples

Here are the MCQs on reported speech for class 10th students. Students have to select the correct option from the given options according to the statement asked based on Reported Speech.

Which sentence is in the reported speech?

a. She said, “I will be there soon.”

b. She says, “I will be there soon.”

c. She said, “She will be there soon.”

d. She says, “She will be there soon.”

What is the correct reported speech for: “I am studying for exams.”?

a. He said that he was studying for exams.

b. He says that he is studying for exams.

c. He says that he was studying for exams.

d. He said that he is studying for exams.

Which pronoun change is correct in reported speech?

a. “I” changes to “he.”

b. “They” changes to “we.”

c. “You” changes to “she.”

d. “He” changes to “it.”

What is the reported speech for: “Did you finish your homework?”?

a. She asked if she finished her homework.

b. She asked if I finished my homework.

c. She asked if I had finished my homework.

d. She asked if she had finished her homework.

Which tense change is required in reported speech?

a. Present simple changes to past simple.

b. Past simple changes to present continuous.

c. Present continuous changes to future perfect.

d. Future simple changes to past perfect.

Which sentence is correctly reported?

a. Sarah told me that she is leaving tomorrow.

b. Sarah told me that she was leaving tomorrow.

c. Sarah tells me that she will leave tomorrow.

d. Sarah told me that she leaves tomorrow.

What is the reported speech for: “I will call you later.”?

a. She said that she would call me later.

b. She said that she would call me later.

c. She says that she will call me later.

d. She says that she will call me later.

Which of the following is a reported speech question?

a. He said, “I am going to the store.”

b. She asked, “Have you seen my keys?”

c. They said, “We will arrive soon.”

d. She told me, “Don’t be late.”

What is the correct reported speech for: “Can you help me with this?”?

a. He asked if he could help me with that.

b. He asked if I can help him with this.

c. He asks if he can help me with this.

d. He asks if I could help him with that.

Which sentence represents reported speech?

a. “Stop!” she shouted.

b. She shouts, “Stop!”

c. She shouted to stop.

d. She shouted, “Stop!”

Also Read: Useful Idioms for IELTS Exams That Will Boost Your Score

Check Your Answers

Match your answers with the right answers given below:

1. c. She said, “She will be there soon.”

2. a. He said that he was studying for exams.

3. a. “I” changes to “he.”

4. c. She asked if I had finished my homework.

5. a. Present simple changes to past simple.

6. b. Sarah told me that she was leaving tomorrow.

7. b. She said that she would call me later.

8. b. She asked, “Have you seen my keys?”

9. a. He asked if he could help me with that.

10. c. She shouted to stop.

Also Read: 50 Examples of Direct and Indirect Speech Interrogative Sentences

As candidates are well versed with the concept of reported speech it is time for the candidates to solve this exercise based on converting direct speech to indirect speech.

“I am reading a book,” she said.
“We will go to the beach tomorrow,” he announced.
“Can you help me with my homework?” she asked.
“I have already seen that movie,” he claimed.
“Please turn off the lights,” she requested.
“They are cooking dinner,” he mentioned.
“Why did you arrive late?” she inquired.
“I cannot solve this math problem,” he admitted.
“I will call you later,” she promised.
“Let’s meet at the park,” he suggested.
“She has been working all day,” he observed.
“Do you like chocolate ice cream?” she wondered.
“The concert starts at 8 PM,” he informed.
“We won the championship,” she exclaimed.
“I need more time to finish the project,” he confessed.
“The train departs in 15 minutes,” she reminded.
“Did you visit the museum?” he asked.
“I’m going to visit my grandparents next weekend,” she said.
“We should plant more trees,” he advised.
“Don’t forget to buy milk,” she reminded.

Must Read: Subject-Verb Agreement: Definition, 12 Rules & Examples

Answers

She said that she was reading a book.
He announced that they would go to the beach the next day.
She asked if I could help her with her homework.
He claimed that he had already seen that movie.
She requested to turn off the lights.
He mentioned that they were cooking dinner.
She inquired why I had arrived late.
He admitted that he couldn’t solve that math problem.
She promised that she would call later.
He suggested meeting at the park.
He observed that she had been working all day.
She wondered if I liked chocolate ice cream.
He informed me that the concert started at 8 PM.
She exclaimed that they had won the championship.
He confessed that he needed more time to finish the project.
She reminded me that the train departed in 15 minutes.
He asked if I had visited the museum.
She said she was going to visit her grandparents the following weekend.
He advised that they should plant more trees.
She reminded me not to forget to buy milk.

More Reads on Reported Speech for Class 10

What are the four types of reported speech?

The four types of reported speech are assertive, interrogative, imperative, and exclamatory.

What are the two main types of reported speech?

The two main types of reported speech are direct and indirect speech.

Why do we use reported speech?

Reported Speech is effective in conveying the thoughts and ideas of others accurately and without causing any misrepresentation.

This was all about the Reported Speech Exercises for Class 10 Students with Answers. Hope you understand the concept and where it’s used. Keep an eye on Leverage Edu for more exciting and informative blogs.

Amanpreet Kaur

📚✨ From Classroom Chats Entered Into The Wordy World ….. Yes , If you all Remember that teacher who kept you on your toes with pop quizzes and endless homework? YEP! THAT WAS Me ! 🌟 But with the blessings of almighty and the key motivation of my husband who came across the spark of writing in me has insisted me to pave my way away from chalk dust to creative burst!💫 Being in this new world of writing I can compose pun-tastic content, poetry full of emotions and humorous articles that can even make Shakespeare envious of me 📝🎭.Yippee! from teaching young minds to educating worldwide readers it's an epic career switch. From teaching grammar lessons to grammatically flawless copy, I'm todays' wordsmith on a mission! Let me spin literary magic all around and conquer my exact destination of proving myself as The Best Writer in The World.🚀🏆 My promise is to provide you with valuable insights, solutions to your questions, and a momentary escape from the routine. I believe in the power of words to create connections, provoke thought, and foster growth. Woods are lovely dark and deep But I have promises to keep and Miles to go before I sleep ……..🌳✨🌌

Leave a Reply Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Contact no. *

Leaving already?

8 Universities with higher ROI than IITs and IIMs

Grab this one-time opportunity to download this ebook

Connect With Us

25,000+ students realised their study abroad dream with us. take the first step today..

Resend OTP in

Need help with?

Study abroad.

UK, Canada, US & More

IELTS, GRE, GMAT & More

Scholarship, Loans & Forex

Country Preference

New Zealand

Which English test are you planning to take?

Which academic test are you planning to take.

Not Sure yet

When are you planning to take the exam?

Already booked my exam slot

Within 2 Months

Want to learn about the test

Which Degree do you wish to pursue?

When do you want to start studying abroad.

January 2024

September 2024

What is your budget to study abroad?

How would you describe this article ?

Please rate this article

We would like to hear more.

Have something on your mind?

Make your study abroad dream a reality in January 2022 with

India's Biggest Virtual University Fair

Essex Direct Admission Day

Why attend .

Don't Miss Out

CBSE Class 10 English Grammar – Direct And Indirect Speech

CBSE Class 10 Science CBSE Class 10 Social Science CBSE Class 10 Maths

(Statements, Commands, Requests, and Questions) The words spoken by a person can be reported in two ways—Direct and Indirect. When we quote the exact words spoken by a person, we call it Direct Speech.

Sohan said to Mohan, “I am going to school.”

The exact words spoken by Sohan are put within inverted commas. But when we give the substance of what Sohan said, it is called the Indirect Speech.

Direct and Indirect Speech

Sohan told to Mohan that he (Sohan) was going to school.

1. Reporting Clause and Reported Speech: Sohan told Mohan that he was going to school. The words which generally come before the inverted commas are called the reporting clause, i.e. Sohan said to Mohan and the verb ‘said’, is called the reporting verb. The words spoken by Sohan and put within inverted commas are called the reported speech, i.e. “I am going to school.”

2. Rules for Changing Direct Speech into Indirect Speech:

In the Indirect speech, no inverted commas are used.
The conjunctions that, if, whether, are generally used after the reporting verb.
The first word of the reported speech begins with a capital letter.
The tense of the reporting verb is never changed.
The reporting verb changes according to sense: it may be told, asked, inquired

3. Rules for the Change of Pronouns:

The first person pronouns (I, me, my, we, us, our) in the reported speech change according to the subject of the reporting verb.
The pronouns of the second person (you, your, yourself) in the reported speech change according to the object of the reporting verb.
The pronouns of the third person do not change.

For example:

He said, “I like the book.” He said that he liked the book.
He said to me, “Do you like the book?” He asked me if I liked the book.
He said, “He likes the book.”a He said that he liked the book.

If the reporting verb is in the present or the future tense, the tense of the reported speech is not changed: Satish says, “I am flying a kite.” Satish says that he is flying a kite. Satish will say, “I want a glass of milk.” Satish will say that he wants a glass of milk.

If the direct speech expresses a historical fact, universal truth, or a habitual fact, then the tense of the direct speech will not change: Direct: He said, “Honesty is the best policy.” Indirect: He said that honesty is the best policy. Direct: He said, “The sun rises in the east.” Indirect: He said that the sun rises in the east. Direct: Rakesh said, “I am an early riser.” Indirect : Rakesh said that he is an early riser. Direct: She said, “God is omnipresent.” Indirect: She said that God is omnipresent. Direct: The teacher said, “The First World War started in 1914.” Indirect: The teacher said that the First World War started in 1914.

6. Changing Statements into Indirect Speech:

The reporting verb ‘said to’ is changed-to ‘told’, ‘replied’, ‘remarked’,
The reporting verb is not followed by an object, it is not changed.
The inverted commas are removed. The conjunction is used to connect the reporting clause with the reported speech.

The rules for the change of pronouns, tenses, etc. are followed.

Direct: Ramu said, “I saw a lion in the forest.” Indirect: Ramu said that he had seen a lion in the forest.
Direct: Satish said to me, “I am very happy here.” Indirect: Satish told me that he was very happy there.
Direct: He said, “I can do this work.” Indirect: He said that he could do that work.
Direct: Renu said to me, “I was washing the clothes.” Indirect: Renu told me that she had been washing the clothes.
Direct: She said, “I am not well.” Indirect: She said that she was not well.
Direct: He said to Sita, “I have passed the test.” Indirect: He told Sita that he had passed the test.
Direct: I said to my friend, “He has been working very hard.” Indirect: I told my friend that he had been working very hard.
Direct: My friend said to me, “I shall go to Delhi tomorrow.” Indirect: My friend told me that he would go to Delhi the next day.
Direct: I said, “I agree to what he said.” Indirect: I said that I agreed to what he had said.
Direct: The student said to the teacher, “I am sorry that I am late.” Indirect: The student told the teacher that he was sorry that he was late.

7. Rules for the Change of Interrogative (Questions) sentences:

The reporting verb “say’ is changed into ask, inquire,

The interrogative sentence is changed into a statement by placing the subject before the verb and the full stop is put at the end of the sentence.

If the interrogative sentence has a wh-word (who, when, where, how, why, etc) the wh-word is repeated in the sentence. It serves as conjunction.

If the interrogative sentence is a yes-no answer type sentence (with auxiliary verbs am, are, was, were, do, did, have, shall, etc), then ‘if or ‘whether’ is used as a conjunction.

The auxiliaries do, does, did in a positive question in the reported speech are dropped.

The conjunction is not used after the reporting clause.

Direct: I said to him, “Where are you going?” Indirect: I asked him where he was going.
Direct: He said to me, “Will you go there?” Indirect: He asked me if I would go there.
Direct: My friend said to Deepak, “Have you ever been to Agra?” Indirect: My friend asked Deepak if he had ever been to Agra.
Direct: I said to him, “Did you enjoy the movie?” Indirect: I asked him if he had enjoyed the movie.
Direct: I said to her, “Do you know him?” Indirect: I asked her if she knew him.
Direct: He said to me, “Will you listen to me?” Indirect: He asked me if I would listen to him.
Direct: I said to him, “When will you go there?” Indirect: I asked him when he would go there.
Direct: He said to me, “How is your father?” Indirect: He asked me how my father was.
Direct: I said to him, “Are you happy?” Indirect: I asked him if he was happy.
Direct: He said to her, “Do you like apples?” Indirect: He asked her if she liked apples.

8. Changing Commands and Requests into Indirect Speech:

In imperative sentences having commands, the reporting verb is changed into command, order, tell, allow, request,etc.
The imperative mood is changed into the infinitive mood by putting ‘to’, before the verb. In case of negative sentences, the auxiliary ‘do’ is dropped and ‘to’ is placed after ‘not’:
Direct: She said to me, “Open the window.” Indirect: She ordered me to open the window.
Direct: The captain said to the soldiers, “Attack the enemy.” Indirect: The captain commanded the soldiers to attack the enemy.
Direct: I said to him, “Leave this place at once.” Indirect: I told him to leave that place at once.
Direct: The teacher said to the students, “Listen to me attentively.” Indirect: The teacher asked the students to listen to him attentively.
Direct: The Principal said to the peon, “Ring the bell.” Indirect: The Principal ordered the peon to ring the bell.
Direct: The master said to the servant, “Fetch me a glass of water.” Indirect: The master ordered the servant to fetch him a glass of water.
Direct: I said to him, “Please bring me a glass of water.” Indirect: I requested him to bring me a glass of water.
Direct: I said to my friend, “Please lend me your book.” Indirect: I requested my friend to lend me his book.

9. Sentences with ‘Let’.

‘Let’ is used in various meanings.

(i) ‘Let’ is used to make a proposal.

First change the reporting verb into ‘proposed’ or ‘suggested’.
Use ‘should’ instead of ‘let’. Example: Direct: He said to me, “Let us go home.” Indirect: He suggested to me that we should go home.

(ii) ‘Let’ is used as ‘to allow’.

In Indirect Speech, we change the reporting verb to ‘requested’ or ‘ordered’.
We start Reported Speech with ‘to’. Direct: Ram said to Mohan, “Let him do it.” Indirect: Ram ordered Mohan to let him do that. Or Ram told Mohan that he might be allowed to do that.

10. Sentences with Question Tags (i) In the indirect speech the question-tag is usually left. (ii) In indirect speech these words are removed and the word ‘respectfully’ is used in the reporting clause. Direct: Mahesh said, “Sir, may I go home?” Indirect: Mahesh respectfully asked his sir if he might go home.

11. Sentences with ‘Yes’ or ‘No’ Direct : He said, “Can you dance?” And I said, “No.” Indirect: He asked me if I could dance and I replied that I couldn’t. Direct : My mother said, “Will you come home on time?” And I said, “Yes.” Indirect: My mother asked me if I would come home on time and I replied that I would.

Note : ‘Yes’ of ‘No’ hides a complete sentence. Therefore, change yes/no into a short answer.

Direct : She said to me, “You didn’t break the window, did you?” Indirect: She asked me if/whether I had broken the window. Direct : He said to Geeta, “You are going to the station, aren’t you?” Indirect: He asked Geeta if/ whether she was going to the station.

12. Sentences with ‘have to’ or ‘had to’ (i) Change ‘have to’ according to the rules. (ii) But change ‘had to’ into ‘had had to’ in the indirect speech. Direct : Hari said, “I have to work a lot.” Indirect: Hari said that he had to work a lot. Direct : Hari said, “I had to work a lot.” Indirect: Hari said that he had had to work a lot.

13. Sentences with ‘Sir’, ‘Madam’ or ‘Your Honour’ etc.

Generally such words are used to show respect to the person concerned.

You can master in English Grammar of various classes by our articles like Tenses, Clauses, Prepositions, Story writing, Unseen Passage, Notice Writing etc.

14. Exclamations and Wishes Sometimes Exclamatory sentences contain exclamations like Hurrah!, Alas!, Oh!, Heavens!, Bravo, etc. Such exclamatory words are removed in the indirect speech and we use ‘exclaimed with sorrow’, exclaimed with joy, exclaimed with surprise, etc. instead of ‘said’. Examples:

Direct : Rohan said, “Hurrah! We won the match.” Indirect: Rohan exclaimed with joy that they had won the match.
Direct : Reema said, “Alas! Karina’s mother is suffering from cancer.” Indirect: Reema exclaimed with sorrow that Karina’s mother was suffering from cancer.
Direct : The captain said to Kapil, “Bravo! You scored 89 runs.” Indirect: The captain exclaimed with praise that he (Kapil) had scored 89 runs.

(a) Look at these sentences.

Direct : My mother said, “May God bless you!” Indirect: My mother prayed to God for my well being.
Direct : She said, “May God save the country!” Indirect: She prayed to God to save the country.
Direct : They said to the king, “Long live!” Indirect: They blessed the king for his long life.

(b) Look at these sentences.

Direct : Mohan said, “What a pity!” Indirect: Mohan exclaimed that it was a great pity.
Direct : I said, “How stupid he is!” Indirect: I exclaimed that it was a very stupid of him.
Direct : “What a terrible sight it is!” said the traveller. Indirect: The traveller exclaimed that it was a very terrible sight. All the sentences in inverted commas are exclamatory sentences.

(i) Use ‘exclaimed’ in place of ‘said’ in the reporting verb in the indirect speech. (ii) In Indirect sentences, we use exclamatory sentences as statements. (iii) Indirect speech begins with that and full stop (•) is used instead of the exclamation mark (!). Exercise (Solved)

Change the following sentences into Indirect Speech:

(i) He said, “I will do it now.” Answer: He said that he would do it then.

(ii) He says, “Honesty is the best policy.” Answer: He says that honesty is the best policy.

(iii) Ramesh says, “I have written a letter.” Answer: Ramesh says that he has written a letter.

(iv) She said, “Mahesh will be reading a book.” Answer: She said that Mahesh would be reading a book.

(v) She said, “Where is your father?” Answer: She inquired where his father was.

(vi) He said to me, “Please take your book.” Answer: He requested me to take my book.

(vii) The Principal said to the peon, “Let this boy go out.” Answer: The Principal ordered the peon to let that boy go out.

(viii) He said to me, “May you live long!” Answer: He prayed that I might live long.

(ix) She said, “Goodbye friends!” Answer: She bade goodbye to her friends.

(ix) The student said, “Alas! I wasted my time last year.” Answer: The student regretted that he had wasted his time the previous year. Exercise (Unsolved)

The captain said, “Bravo! well done, my boys.”
He said to her, “Why do you read this book?”
He said to her, “Does your cow not kick?”
He said to his brother, “Shailesh has broken my glass.”
Our teacher said, “The earth revolves around the sun.”
He said to me, “Why have you come here?”
Usha said, “Father, you are very kind to me.”
The teacher said to the boys, “Do not make a noise.”
He said to his friend, “May you prosper in business!”
The officer said to the peon, “Let the visitor come into my office.”

When we want to tell somebody else what another person said, we can use either direct speech and reported speech. When we use direct speech, we use the same words but use quotation marks, For example: Scott said, “I am coming to work. I will be late because there is a lot of traffic now.”

When we use reported speech, we usually change the verbs, specific times, and pronouns. For example: Scott said that he was coming to work. He said that he would be late because there was a lot of traffic at that time.

Reported Speech Exercises for Class 10 CBSE With Answers

This grammar section explains English Grammar in a clear and simple way. There are example sentences to show how the language is used. NCERT Solutions for Class 10 English will help you to write better answers in your Class 10 exams. Because the Solutions are solved by subject matter experts.

Rules for Reported Speech While changing direct speech into reported speech or vice-versa the following changes occur:

1. Changes In Reporting Verb Affirmative sentences: said, told (object), asserted, replied, assured, informed, responded, whispered, alleged, believed, assumed, thought Interrogative sentences: asked, enquired, wanted to know Imperative sentences: ordered, begged, pleaded, implored, advised, demanded

2. Change Of Pronouns Direct Speech: Johnny said, ‘I am playing.’ Indirect Speech: Johnny said that he was playing. First-person generally changes to third person {depending upon the subject of the reporting verb).

3. Change Of Tenses

In general, present tense becomes past tense; past tense and present perfect become past perfect.

4. Change of situations Example: Nagesh said, ‘I read this book last week. (direct speech) Nagesh said that he had read that book the previous week, (indirect speech)

‘this’ becomes ‘that’
‘last week’ becomes ‘the previous week’
here – there
today – that day
yesterday – the day before/the previous day
tomorrow – the next day/the coming day
last week – the week before/the previous week
next month – the next month/the coming month

5. In case of questions and answers Examples:

Nagesh asked, ‘Have you read this book?’ (direct speech)
Nagesh asked if’ whether I had read that book, (indirect speech)
Nagesh asked, ‘Where is the book?’ (direct speech)
Nagesh asked where the book was. (indirect speech)

(a) For yes/no questions – use if/whether (b) For wh- questions – use the wh-word

Word Order:

Nagesh asked, ‘What’s the matter?’
Nagesh asked what the matter was. (what + the matter + was)
Nagesh asked what was the matter, (what + was + the matter)
The word order can be either:
who/which/what + complement + be or ‘
who/which/what + be + complement

6. Reported Speech using present and future tenses Examples:

Nagesh said, ‘The sun rises in the east.’ (direct speech)
Nagesh said that the sun rises in the east, (indirect speech)
Nagesh said, ‘I will read this book.’ (direct speech)
Nagesh said that he will read that book, (indirect speech)
If the original speaker’s present and future is still present and future, the tense remains unchanged.

7. In case of modal verbs can becomes could

will – would
shall – should
may – might

would, should, could, might, ought to and must are unchanged. Example:

Nagesh said, ‘I can solve this sum.’ (direct speech)
Nagesh said that he could solve that sum. (indirect speech)

Reported Speech Solved Examples Exercises for Class 10 CBSE

Read the dialogue given below and then complete the passage that follows.

Question 1. Read the dialogue and complete the passage given below.

Interviewer: So, why do you want to be a computer programmer? Ravi: Well, I have always been interested in computers. Interviewer: I see. Do you have any experience? Ravi: No, but I’m a fast learner. Interviewer: What kind of a computer do you use? Ravi: Computer? Uhm, let me see. I can use a Mac. I also used Windows 10 once. Interviewer: That’s good.

Ravi recently attended an interview for the selection of a computer programmer. At the interview, he was asked (a) ……………………….. To this question he replied that he wanted to change his job because (b) ……………………….. When the interviewer asked him (e) ………………………. he replied that he (d) ……………………….. Finally, the interviewer wanted to know (e) ………………………. . Ravi replied that he could use a Mac and had also used Windows 10 once in the,.past. The interviewer seemed to be pleased with his answers. Answer: (a) why he wanted to be a computer programmer (b) he had always been interested in computers (c) whether he had any experience (d) didn’t but that he was a fast learner (e) the kind of computer he used

Question 2. Manu: Where are you going to? Annu: I am going to the market. Do you want anything?

Manu asked Annu (a) …………………… Annu replied (b) …………………… Annu replied (b) …………………… and she further asked (C) …………………… Answer: (a) where she was going. (b) that she was going to the market (c) if/whether she wanted anything.

Question 3. Sunita: Tomorrow is your birthday, what do you want as a gift? Neetu: That is a lovely thought but I don’t want anything.

Sunita asked Neetu since the next day was her birthday, (a) …………………… Neetu replied that (b) …………………… but (C) ………………….. . Answer: (a) what she wanted as a gift (b) that was a lovely thought (c) she did not want anything.

Question 4. Gardener: Did you water the plant today? Dev: No, but I will, today. Gardener: Then tomorrow I will get a sapling of sunflower.

The Gardener asked Dev (a) …………………… Dev replied negatively but (b) …………………… Then the gardener said that (c) ………………….. . Answer: (a) if/whether he had watered the plant that day. (b) said he would that day. (c) he would get a sapling of a sunflower the next day.

Question 5. Mr. Harish: Can you polish my shoes? Cobbler: Yes sir. But I will take 10 for each shoe.

Mr. Harish: I will not mind as long as it is done. Mr. Harish asked the cobbler (a) …………………… The cobbler replied affirmatively but (b) …………………… Mr. Harish said that (C) ……………………. Answer: (a) if/whether he could polish his shoes. (b) said that he would take 10 for each shoe (c) he would not mind as long as it was done.

Question 6. Electrician: When did your electricity go? Mohan: It is not working since evening. Electrician: Sorry sir, in this case, I will have to check the fuse now.

The electrician asked Mohan (a) …………………… Mohan replied that (b) …………………… The electrician apologetically said that in that case (c) …………………… Answer: (a) when his electricity had gone. (b) it was not working since evening. (c) he would have to check the fuse then.

Question 7. Teacher : Children, let us all pledge to save trees. Children : Yes, mam, we all pledge to save our trees as the trees are the lungs of the city. Teacher : Let us start today by planting a sapling.

The teacher asked all the children to pledge to save trees. The children replied affirmatively (a) …………………… as the (b) …………………… Then the teacher said that (c) ………………….. . Answer: (a) saying that they all pledged to save trees (b) trees are the lungs of the city. (c) they should start by planting a sapling that day.

Question 8. Buddha : Honesty is the best policy. Disciple : Does honesty always pay? Buddha : It may or may not, but at least you will never feel guilty.

Buddha in his preaching said that (a) …………………… the best policy. A disciple asked him if (b) …………………… always pays, Buddha replied (c) …………………… but at least he would never feel guilty. Answer: (a) Honesty is (b) honesty (c) that it might or might not

Question 9. Doctor : You should take this medicine every day. Patient : Should I take it before dinner or after dinner? Doctor : No, you should take it after breakfast.

The Doctor advised the patient that (a) …………………… The patient further asked (b) …………………… The doctor replied negatively and then said (c) ………………….. . Answer: (a) he should take that medicine every day. (b) if/whether he should take it before dinner or after dinner. (c) that he should take it after breakfast

Question 10. Reena : Do you know how to swim? Surbhi : Yes I know. I have learnt it during this summer vacation.

Reena asked Surbhi (a) …………………… Then Surbhi replied (b) …………………… and also added that (c) ………………….. . Answer: (a) if/whether she knew how to swim (b) in affirmative (c) she had learnt it during the summer vacation.

Question and Answer forum for K12 Students

Reported Speech Exercises for Class 10 CBSE

Reported Speech Dialogue Exercises for Class 10 CBSE With Answers

Reported speech is when we express or say things that have already been said by somebody else.

Basic English Grammar rules can be tricky. In this article, we’ll get you started with the basics of sentence structure, punctuation, parts of speech, and more.

We also providing Extra Questions for Class 10 English Chapter wise.

Reported Speech Dialogue Exercises For Class 10 Cbse With Answers PDF

Reporting of the words of a speaker in one’s own words is called Narration. There are two ways of reporting what people say: Direct Speech and Indirect Speech. Direct Speech. The actual words of the speaker using quotation marks are called Direct Speech. Indirect Speech. When we convey the speaker’s words in our own words it becomes Indirect Speech. It is the reporting of speakers’ words, using a saying or asking verbs. In indirect, verbs giving or asking for instructing are often used with a to-infinitive construction. Verbs expressing intention may also be followed by a to-infinitive.

There are basically four types of sentences in which we can convert direct speech into Indirect speech.

Assertive Sentences (Statements)
Interrogative Sentences (Questions)
Imperative Sentences (Commands and Requests)
Exclamatory Sentences (Strong Feelings)

To convert a Direct speech into an Indirect speech, we have to make some necessary changes. Change No.1. Remove the commas and inverted commas. Use any conjunction.

Change No.2. In Reported Speech, there are some words which show nearness, but they are always converted into words which show distance.

They are as follows:

Note. ‘Come’ is changed into ‘go’ only in that case when any word showing nearness is given with it. Change No. 3. Change of Person. There are three types of Person in English language which are as follows:

Change No. 4. If the reporting verb is in Present or in Future Tense, there is no change in the tense of the Reported Speech. If the reporting verb is in Past Tense, there is always a change in the tense of the Reported Speech, which is as follows:

Present Indefinite is changed into Past Indefinite
Present Continuous is changed into Past Continuous
Present Perfect is changed into Past Perfect
Present Perfect Continuous is changed into Past Perfect Continuous
Past Indefinite is changed into Past Perfect
Past Continuous is changed into Past Perfect Continuous
Past Perfect and Past Perfect Continuous remain unchanged

In case of Future Tense, there are only four words which are changed, i.e.

Changes based on the types of sentences.

1. Assertive Sentences (Statements) Change No. 1. Remove the commas and inverted commas. Use conjunction ‘that’. Change No. 2. Change the reporting verb ‘say into tell’, ‘says into tell’, ‘said into told’, if the reporting object is given in the sentence. But do not change the reporting verb if the reporting object is not given in the sentence. Change No. 3. ‘Said to’ can be changed into told, replied, informed, stated, added, remarked, asserted, assured, reminded, complained, and reported, according to the meaning. Change No. 4. Always remove “to’ from the reporting speech, e.g.

He said to me, “I cannot help you in this matter.” He told me that he could not help me in that matter.
He said, “My sister’s marriage comes off next month.” He said that his sister’s marriage would come off the following month.

2. Interrogative Sentences (Questions)

Change No. 1. Change the reporting verb ‘said ‘or ‘said to’into‘ asked’ or ‘inquired of’. In case of a single question, change it into ‘asked’ but in case of more than one question, change it into “inquired of’. Change No. 2. Use conjunction ‘if’ or ‘whether’ if the reported speech starts with a helping verb. But do not use any conjunction if the reported speech starts with an interrogative word. Change No. 3. Change the Interrogative sense into an Assertive sense. Change No. 4. Remove ‘?’ question mark and use ‘ . ‘full stop, e.g.

She said to her servant, “Is tea ready for me?” She asked her servant if tea was ready for her.
She asked me, “Who teaches you English?” She asked me who taught me English.

3. Imperative Sentences (Commands and Requests)

Change No. 1. Change the reporting verb‘said’ or ‘said to’ into ordered, commanded, requested, advised, warned, forbade, suggested, encouraged, persuaded, begged, etc. according to the sense. Change No. 2. Remove the commas and inverted commas, use conjunction ‘to’. Change No. 3. Change the Imperative sense into Infinitive sense. Change No. 4. Remove ‘do not and use ‘not to’ in case of Negative Imperative sentences, e.g.

The teacher said to me, “Stand up on the bench.”. The teacher ordered me to stand up on the bench.
The General said to the soldiers, “March forward and attack the foe.” The General ordered the soldiers to march forward and attack the foe.
The gardener said to the boys, “Do not pluck the flowers.” The gardener forbade the boys from plucking the flowers.

4. Exclamatory Sentences (Strong Feelings)

Change No. 1. Change the reporting verb ‘said’ or ‘said to’ into “exclaimed with joy’ or ‘exclaimed with sorrow’, ‘cry out, “pray’, etc., according to the sense, i.e.

Exclaimed with joy–in case of Aha! Ha! Hurrah!
Exclaimed with sorrow–in case of Ah! Alas!
Exclaimed with surprise–in case of Oh! What! How!
Exclaimed with regret–in case of Sorry!
Exclaimed with contempt–in case of Pooh! Pshaw!
Applauded with saying–in case of Bravo! Hear!

Change No. 2. Use very or great by removing what or how. Change No. 3. Use conjunction ‘that. Change No. 4. Remove exclamatory word and exclamation sign ‘!’ The student must select the verb best suited to the sense or context, e.g.

They said, “Hurrah! We have won the match.” They exclaimed with joy that they had won the match.
She said, “Alas! I have lost my bridal ring.” She exclaimed with sorrow that she had lost her bridal ring.
She said, “How charming the scenery is!” She exclaimed with surprise that it was a very charming scenery.

Reported Speech Exercises Solved Example With Answers for Class 10 CBSE

Diagnostic Test – 29

Mother: Why are you looking so worried? Daughter: My exams are approaching. Mother: When will they start? Daughter: Next month, Mother.

Mother asked her daughter (a) …………………… The daughter replied that (b) …………………… Mother further asked (c) …………………… The daughter told her mother that they would start in the following month.

Answer: (a) why she was looking very worried (b) her exams were approaching. (c) when they would start.

Grammar | Reported Speech

In the chapter "Reported Speech," students learn how to report what someone else has said. This involves changing the tense of the original sentence, as well as making changes to pronouns, time expressions, and other words.

Questions & Answers

Introduction to CBSE Solutions for Class 10 English Chapter: Reported Speech

The chapter “Reported Speech” teaches students how to report statements, questions, and requests made by others. It explains the changes that occur when reporting speech, such as changes in verb tense, pronouns, and time expressions. The chapter also covers the use of reporting verbs and the rules for reporting different types of sentences. By the end of the chapter, students should be able to accurately report what someone else has said in both written and spoken English.

Assignment and Activities for CBSE Class 10 English Chapter: Reported Speech

Reporting Speech: Listen to a conversation or a speech and write a report summarizing what was said.
Dialogue Rewrite: Rewrite a dialogue in reported speech, making the necessary changes in verb tense, pronouns, and time expressions.
Reporting Questions: Practice reporting questions by changing direct questions into reported questions.
Reporting Requests: Report requests made by others, ensuring the correct use of reporting verbs and changes in verb form.
Mixed Sentences: Create a set of sentences that includes statements, questions, and requests, and then report them using reported speech.
Reported Speech Game: Play a game where one student reports a statement to another student, who then has to report it to a third student, and so on.
Interview Reporting: Conduct a mock interview and then report the questions and answers using reported speech.
Reported Speech Quiz: Prepare a quiz for your classmates to test their understanding of reported speech.
Storytelling: Tell a short story using reported speech to report what the characters say.
Reported Speech in Literature: Find examples of reported speech in a piece of literature and analyze how the author uses it to convey information.

Conclusion : Reported Speech

The chapter “Reported Speech” is an essential part of learning English grammar. By understanding how to report what others have said, students can communicate more effectively and accurately in both written and spoken English. Through practice and application, students can master the rules of reported speech and use them confidently in their communication.

"Preparing for the Class 6 exam? Notebook is your go-to resource for learning anytime, anywhere. With courses, docs, videos, and tests covering the complete syllabus, Notebook has the perfect solution for all your study needs. Join Notebook today to get everything you need in one place.

Questions and Answers for CBSE Class 10 English Chapter: Reported Speech

Q1. What is reported speech?

ANS: Reported speech is when we report what someone else has said without quoting their exact words.

Q2. What are the changes that occur in reported speech?

ANS: Changes in verb tense, pronouns, time expressions, and other words occur in reported speech.

Q3. How do you report statements in reported speech?

ANS: Statements are reported by changing the verb tense, pronouns, and other words as necessary.

Q4. Can you report questions in reported speech?

ANS: Yes, questions can be reported by changing them into reported questions.

Q5. What are reporting verbs?

ANS: Reporting verbs are verbs used to report what someone else has said, such as “say,” “tell,” “ask,” etc.

Q6. How do you report requests in reported speech?

ANS: Requests are reported by using reporting verbs such as “ask,” “request,” or “beg,” and changing the verb form as necessary.

Q7. What is the importance of reported speech in English?

ANS: Reported speech is important because it allows us to report what others have said accurately and effectively.

Q8. How do you report commands in reported speech?

ANS: Commands are reported by using reporting verbs such as “tell” or “order,” and changing the verb form as necessary.

Q9. What are the common mistakes to avoid in reported speech?

ANS: Common mistakes include incorrect changes in verb tense, pronouns, and word order.

Q10. How can you improve your reported speech skills?

ANS: You can improve your reported speech skills by practicing reporting different types of sentences and paying attention to the changes that occur.

Want access to all premium tests, videos & docs?

Learn next topic:, economics | sectors of the indian economy, previous post political science | what is democracy why democracy, next post writing section | story writing.

Comments are closed.

Augmented Storytelling

Welcome to the world of beautiful hand drawn picures

Get maximum value for each minute spent

Content Pedagogy

Generating curiosity- maximising takeaway

Download the Mobile App to watch more videos.

In Partnership with Notebook.

In Partnership with

Quick Links

About Notebook
Enquiry about Notebook
Notebook Media
Notebook Courses

+91 080 41650688

[email protected].

Reported Speech Exercises for Class 10 CBSE With Answers

In Online Education When we want to tell somebody else what another person said, we can use either direct speech and reported speech. When we use direct speech, we use the same words but use quotation marks, For example: Scott said, “I am coming to work. I will be late because there is a lot of traffic now.”

Online Education for Reported Speech Exercises for Class 10 CBSE With Answers Pdf

Reported Speech Class 10 Exercise

Rules for Reported Speech While changing direct speech into reported speech or vice-versa the following changes occur:

3. Change Of Tenses

In general, present tense becomes past tense; past tense and present perfect become past perfect.

Reported Speech Exercises For Class 10

4. Change of situations Example: Nagesh said, ‘I read this book last week. (direct speech) Nagesh said that he had read that book the previous week, (indirect speech)

‘this’ becomes ‘that’
‘last week’ becomes ‘the previous week’
here – there
now – then
today – that day
yesterday – the day before/the previous day
tomorrow – the next day/the coming day
last week – the week before/the previous week
next month – the next month/the coming month

5. In case of questions and answers Examples:

Nagesh asked, ‘Have you read this book?’ (direct speech)
Nagesh asked if’ whether I had read that book, (indirect speech)
Nagesh asked, ‘Where is the book?’ (direct speech)
Nagesh asked where the book was. (indirect speech)

(a) For yes/no questions – use if/whether (b) For wh- questions – use the wh-word

Word Order:

Nagesh asked, ‘What’s the matter?’
Nagesh asked what the matter was. (what + the matter + was)
Nagesh asked what was the matter, (what + was + the matter)
The word order can be either:
who/which/what + complement + be or ‘
who/which/what + be + complement

6. Reported Speech using present and future tenses Examples:

Nagesh said, ‘The sun rises in the east.’ (direct speech)
Nagesh said that the sun rises in the east, (indirect speech)
Nagesh said, ‘I will read this book.’ (direct speech)
Nagesh said that he will read that book, (indirect speech)
If the original speaker’s present and future is still present and future, the tense remains unchanged.

7. In case of modal verbs can becomes could

will – would
shall – should
may – might

would, should, could, might, ought to and must are unchanged. Example:

Nagesh said, ‘I can solve this sum.’ (direct speech)
Nagesh said that he could solve that sum. (indirect speech)

Reported Speech Solved Examples Exercises for Class 10 CBSE

Read the dialogue given below and then complete the passage that follows.

Reported Speech Class 10 Exercise With Answers Question 1. Read the dialogue and complete the passage given below.

Reported Speech Exercises With Answers For Class 10 Question 2. Manu: Where are you going to? Annu: I am going to the market. Do you want anything?

Class 10 Reported Speech Exercises Question 3. Sunita: Tomorrow is your birthday, what do you want as a gift? Neetu: That is a lovely thought but I don’t want anything.

Reported Speech Worksheet For Class 10 Question 4. Gardener: Did you water the plant today? Dev: No, but I will, today. Gardener: Then tomorrow I will get a sapling of sunflower.

Reported Speech Exercise Class 10 Question 5. Mr. Harish: Can you polish my shoes? Cobbler: Yes sir. But I will take 10 for each shoe.

Reported Speech Class 10 Questions Question 6. Electrician: When did your electricity go? Mohan: It is not working since evening. Electrician: Sorry sir, in this case, I will have to check the fuse now.

Reported Speech Questions For Class 10 Question 7. Teacher : Children, let us all pledge to save trees. Children : Yes, mam, we all pledge to save our trees as the trees are the lungs of the city. Teacher : Let us start today by planting a sapling.

Reported Speech Dialogue Exercises With Answers Pdf Question 8. Buddha : Honesty is the best policy. Disciple : Does honesty always pay? Buddha : It may or may not, but at least you will never feel guilty.

Reported Speech Examples With Answers Class 10 Question 9. Doctor : You should take this medicine every day. Patient : Should I take it before dinner or after dinner? Doctor : No, you should take it after breakfast.

Class 10 Reported Speech Question 10. Reena : Do you know how to swim? Surbhi : Yes I know. I have learnt it during this summer vacation.

7. Reported speech

CBSE Class 10
Writing Skills with Grammar
7. Reported speech Notes
Online English tutor English Book
Publication
Online English tutor

Direct and Indirect (reported) Speech

In our daily conversation many times we describe or report an event or action that happened, and very often that includes repeating what someone said.

In order to describe what people said there are two different types of speech –

Direct speech
Indirect speech (or reported speech)

What is Direct & Indirect Speech?

Direct speech – in this type of speech qwe report the message of the speaker in the exact words as spoken by him.

Direct speech example : Maya said ‘I am busy now’.

‘I am busy now’ are the exact words of the speaker.

Indirect speech : In this type of speech we report only the message of the speaker in our own words , we don’t report the exact words of that speaker.

Indirect speech example: Maya said that she was busy then. – only message is reported not the exact words of speaker

Reporting verb and reported speech

The speech of a person can be divided into two categories

Reporting verb (verb of the first clause)
Reported speech ( words within inverted commas

She said, “I will be late today.”

First clause is ‘She said’ where

Said = reporting verb

Words within inverted commas are

‘I will be late today ‘ = reported speech

Important points to convert direct speech into indirect speech

No punctuation marks are used except full stop.
Tense of the reported speech is changed if the reporting verb is in past tense.
No change in the tense of the reported speech if it expresses any universal truth, habitual action or historical facts.

Rules to change the reporting verb

Says – says

Says to – tells

Said – said

Said to- told

Rules to change the Pronouns of reported speech

First person i s change according to the subject of reporting verb
Second person is changed according to the object of the reporting verb
Third person is not changed

Rules to change the Direct speech into Indirect speech ( if the reporting verb is in past)

1. Simple present becomes simple past

Direct - He said, “She often hesitates while speaking in class”.

Indirect - He said that she often hesitates while speaking in class. ( Habitual action thus no change in tense)

Direct - He said, “I go to park”

Indirect - He said that he went to park. (Habitual action thus no change in tense)

Direct - He said, “my mother is in the hospital.”

Indirect - He said that his mother was in the hospital.

2. Present continuous becomes past continuous

Direct - He said, “She is watering the plants.”

Indirect - He said that she was watering the plants.

Direct - He said, “ I am looking for my purse”

Indirect - He said that he was looking for his purse.

3. Present perfect becomes past perfect

Direct - He said, “We have taken our meal.”

Indirect - He said that they had taken their meal.

Direct - He said, “you have won the match.”

Indirect - He said that I had won the match.

4. Present perfect continuous becomes past perfect continuous

Direct - He said, “I have been suffering with this disease since last week.”

Indirect - He said that he had been suffering with that disease since the previous week.”

Direct - He said, “My parents have been looking for new accommodation for two months. ”

Indirect - He said that his parents had been looking for new accommodation for two months.

5. Simple past becomes past perfect

Direct - He said, “We visited here a month ago.”

Indirect - He said that they had visited there a month before.

Direct - He said to us, “The police didn’t arrest the thief today.”

Indirect - He told us that the police had not arrested the thief that day.

6. Past continuous becomes past perfect continuous

Direct - He said, “These boys were quarrelling today”

Indirect - He said that those boys had been quarrelling that day”

Direct - He said, “you were sleeping when I came.”

Indirect - He said that I had been sleeping when he had come.

7. Past perfect remains unchanged

Direct - He said, “They had gone yesterday.”

Indirect - He said that they had gone a day before.

Direct - He said, “you had slept when I came.”

Indirect - He said that I had slept when he had come.

8. Past perfect continuous remains unchanged

Direct - He said, “I had been riding the bicycle since morning.”

Indirect - He said that he had been riding the bicycle since morning.

Direct - He said, “I had been swimming for a week”

Indirect - He said that he had been swimming for a week.

9. Shall/will/can/may get converted into should/ would/could/might

Direct - He said, “I shall drop you home.”

Indirect - He said that he would drop me home.( Shall is often converted into would )

Direct - He said, “My brother can be coming by bus.”

Indirect - He said that his brother could be coming by bus.

Direct - He said, “ He may have cleaned the home.”

Indirect - He said that he might have cleaned the home.

Direct - He said, “ Raman shall have been walking for an hour.”

Indirect - He said that Raman would have been walking for an hour.

Changes in time or place expressions in direct speech to indirect speech

Words showing nearness in time and place are changed into the words showing distance in time and place.

It depends on when we heard the direct speech and when we report speech. ( Generally at the time of speaking)

if it is Sunday

Mary says "My parents are arriving today ". If Mona tell someone on Sunday

Mona say "Mary said that her parents were arriving today ".

But on Tuesday,

Mona say " Mary said that her parents were arriving yesterday ".

In the same way,

on Wednesday

Mona say "Mary said that her parents were arriving on Monday ". after a week

Mona will say "Mary said that her parents were leaving that day " .

Place words ( should be changed according to the given situation )

When the speech is reported in the same place - no changes in place words
When the speech is reported in the different place - due changes in place words

If the speech is reported at the same place.

He said: "Sam has been here." → He said that Sam had been here.

If the speech is reported at the different place.

He said: "Sam has been here." → He said that Sam had been there .

Direct speech Indirect speech

Here → there, in Starbucks

This → that

this book → the book, that book,

in this room → in the room, in that room

Conversion Of Interrogative sentences into Indirect speech

‘Asked’ is used to introduce reported speech.
The interrogative form is changed into Assertive Forms

The auxiliary verbs is shifted back to the subject.

Direct speech : “Where are you going?” Reported speech : He asked me where I was going . ( Verb shifted back to the subject)

No auxiliary verb is used , except in negative questions.

Direct -I said to him,” Do you know how to dance.”

Reported - I asked him if he knew how to dance. (‘Do' is dropped)

Yes / no questions

structure :

'ask' + 'if / whether' + clause :

Direct speech : Sue said “Shall I ever forget her” Reported speech: Sue asked if she would ever forget her.
Direct speech : He said “Do you have a laptop?” Reported speech : He asked if I had a laptop.

Interrogatives with Question words

structure :

'ask' (or another verb like 'ask') + question word + clause .

Direct speech : Sam said “Why is he wandering here and there ?” Reported speech : Sam asked why he was wandering here and there .
Direct speech : She said “What do you want?” Reported speech : She asked me what I wanted .

Conversion Of Imperative sentences into Indirect speech

Reporting verb is changed into the verbs expressing command request or advise

Ordered, advised, requested, commended, urged, forbade, told, asked etc.

Imperative mood is changed into infinitive mood by putting ‘to" before the verb
In case of a negative imperative, the auxiliary ‘ do’ is dropped and ‘to’ is placed after ‘not’
No conjunction is to be used to introduce the Reported speech

Direct speech - He said to me, “do it.”

Reported speech -He asked me to do it.

Direct speech - The teacher said to the boys, “do not make a noise .”

Reported speech -He asked the teacher ordered the boys not to make a noise.

Direct speech - The teacher said to the boys, “do not waste your time.”

Reported speech – The teacher advised the boys not to waste their time.

Direct speech - The doctor said to the patient, “Do not have sweets, as there is cavity in the teeth.”

Reported speech – The doctor forbade the patient to have sweets as there was cavity in the teeth.

Conversion Of sentences beginning with ‘Let’ into Indirect speech

When Let expresses

A proposal – reporting verb changes into ‘proposed or suggested’ and ‘Let’ is replaced by ‘should’

Direct speech - Mohan said to me, “ Let us go to park .”

Reported speech – Mohan Proposed me that we should go to the park.

Direct speech - I said to my friends, “ Let us wait for the result.”

Reported speech – I proposed my friends that we should wait for the result.

A permission – it changes into ‘might be allowed ’ or simply into ‘ to Let'

Direct speech - He said to the peon, “ Let the visitor come in.”

Reported speech – He ordered the peon that the visitor might be allowed to come in.

Reported speech – He ordered the peon to let the visitor come in.

Conversion Of Optative sentences into Indirect speech

Reporting verb ‘ sa i d’ is changed into ‘ wished or prayed’
‘ That’ is used to introduce the reported speech
optative sentence changed into an assertive sentence and the sign of exclamation ‘!’ is replaced by a full stop.

Direct speech - He said to me, “May you be happy.”

Reported speech – He wished that I might be happy.

Direct speech - They said, “Would that I were rich.”

Reported speech – He wished that he had been rich.

Enter your mobile number to sign up in EduMple

Enter your mobile number.

Talk to our experts

1800-120-456-456

Direct and Indirect Speech Class 10 CBSE English

Direct and Indirect Speech Exercises for Class 10 with Answers - Download Free PDF With Solutions

In English, there are mainly two ways to express the spoken words between two individuals. They are direct speech and indirect or reported speech. These two types of speeches narrate the spoken words differently. Do you know about direct and indirect speech or Reported Speech Class 10 ? Are you able to convert direct speech into indirect speech and vice versa? If not, direct and indirect speech exercises for class 10 pdf with answers will help you learn direct and indirect speeches with ease and will leave no room for doubts.

Direct and Indirect Speech Exercises for Class 10 with Answers

Direct speech.

Direct speech refers to the speech with the speaker's actual words. This type of speech has the word-to-word restatement of the speaker's speech.

Example: Rahim said, "I am going to the playground."

Indirect or Reported Speech Class 10

Indirect or reported speech refers to the speech that doesn't use the actual word-to-word statement of the speaker. Also, indirect speech follows past tense, generally.

Example: Rahim said he was going to the playground.

Benefits of Learning Reported Speech Class 10

Mastering the art of direct and indirect speech holds significant importance in the academic journey of Class 10 students. As they navigate through the intricacies of language and communication, understanding the nuances of direct and indirect speech equips them with essential skills for effective expression and comprehension. In this introduction, we unravel the benefits of learning direct and indirect speech, shedding light on its relevance and impact on the academic and linguistic development of Class 10 students. Here are some of the Benefits of Learning Direct and Indirect Speech Exercises for Class 10 with Answers:

Enhances Communication Skills: Learning direct and indirect speech enhances students' communication skills by enabling them to effectively convey messages in various contexts. It empowers them to articulate thoughts, ideas, and narratives with clarity and precision, fostering confident expression in both oral and written communication.

Improves Language Proficiency: Delving into the intricacies of direct and indirect speech enhances students' language proficiency by deepening their understanding of grammar and syntax. It familiarizes them with the rules and conventions governing Reported Speech Class 10 , enabling them to construct grammatically accurate sentences and compositions.

Facilitates Comprehension: Mastery of direct and indirect speech facilitates comprehension as students learn to decipher and interpret statements made by others accurately. It hones their ability to comprehend complex dialogues, narratives, and textual passages, thereby enhancing their reading and comprehension skills.

Enhances Critical Thinking: Engaging with direct and indirect speech prompts students to think critically as they analyze and evaluate different forms of communication. It encourages them to assess the implications of Reported Speech Class 10 , discern underlying meanings, and draw inferences, thereby fostering critical thinking and analytical skills.

Prepares for Academic Success: Proficiency in direct and indirect speech is integral to academic success, especially in subjects like English and languages. It equips Class 10 students with the requisite skills to excel in examinations, comprehension exercises, and language-based assessments, laying a strong foundation for future academic pursuits.

Basic Rules of Direct and Indirect Speech that Students of Class 10 Should Know

In the journey of language acquisition and effective communication, the mastery of direct and indirect speech stands as a pivotal skill set. For students navigating the complexities of language at the Class 10 level, understanding the fundamental rules governing direct and indirect speech is paramount. In this introduction, we delve into the significance of comprehending these rules, equipping students with the necessary knowledge to navigate the intricacies of Reported Speech Class 10 with confidence and precision.

Rules of Direct Speech for Class 10

For every character's speech, use separate lines.

Always start a speech with a capital letter.

Every speaker's speech should be in quotes ("XYZ").

We will use a reported clause (like, 'said,' 'asked,' 'replied') before the quotation.

Rules of Reported Speech Class 10

If the reporting verb of the direct speech is in the past tense, all the present tenses used in direct speech will be in the past tense in the indirect or reported speech.

Present perfect tense and present continuous tense in direct speech will be in the past perfect tense and past continuous tense in indirect or reported speech.

Simple present tense in direct speech will be in simple past in indirect or reported speech.

Simple future and future continuous tense in direct speech changes to the present conditional and conditional continuous tense in indirect speech.

Modals like must, may, and can in direct speech become would have to/ had to, might, and could respectively in indirect speech.

The First-person in direct speech becomes the subject in indirect speech.

The imperative mood becomes the infinitive in Reported Speech Class 10.

Direct to Indirect Speech Conversion

Direct Speech: I said, "I am busy."

Indirect Speech: I said I was busy.

Direct Speech: She said, "Are you okay"?

Indirect Speech: She inquired of you whether you were okay.

Direct Speech: She said, "I will leave now."

Indirect Speech: She said that she would leave then.

Important Topics for Class 10 Direct and Indirect Speech

In this chapter, you will learn:

What are direct and indirect speech?

What are the basic rules of direct and indirect speech?

How to convert direct speech into indirect speech?

Reported Speech Exercises for Class 10 with Answers

This grammar section explains English Grammar clearly and simply. There are example sentences to show how the language is used. Direct and Indirect Speech Exercises for Class 10 with Answers will help you to write better answers in your Class 10 exams. Because the Solutions are solved by subject matter experts.

Rules for Reported Speech Class 10

While changing direct speech into reported speech or vice-versa the following changes occur:

1. Changes In Reporting Verb

Affirmative sentences: said, told (object), asserted, replied, assured, informed, responded, whispered, alleged, believed, assumed, thought

Interrogative sentences: asked, enquired, wanted to know

Imperative sentences: ordered, begged, pleaded, implored, advised, demanded

2. Change Of Pronouns

Direct Speech: Johnny said, 'I am playing.'

Indirect Speech: Johnny said that he was playing.

First-person generally changes to third person {depending upon the subject of the reporting verb).

3. Change Of Tenses

In general, present tense becomes past tense; past tense and present perfect become past perfect.

4. Change of situations Example:

Nagesh said, 'I read this book last week. (direct speech)

Nagesh said that he had read that book the previous week, (indirect speech)

'this' becomes 'that'

'last week' becomes 'the previous week'

here – there

today - that day

yesterday - the day before/the previous day

tomorrow - the next day/the coming day

last week - the week before/the previous week • next month – the next month/the coming month

5. In case of questions and answers

Nagesh asked, 'Have you read this book?' (direct speech)

Nagesh asked if' whether I had read that book, (indirect speech)

Nagesh asked, 'Where is the book?' (direct speech)

Nagesh asked where the book was. (indirect speech)

(a) For yes/no questions - use if/whether

(b) For wh- questions - use the wh-word

Word Order:

Nagesh asked, 'What's the matter?'

Nagesh asked what the matter was. (what + the matter + was) Nagesh asked what was the matter, (what + was + the matter)

The word order can be either:

who/which/what + complement + be or who/which/what + be + complement

6. Reported Speech using present and future tenses Examples:

Nagesh said, "The sun rises in the east. (direct speech)

Nagesh said that the sun rises in the east, (indirect speech)

Nagesh said, 'I will read this book.' (direct speech)

Nagesh said that he will read that book, (indirect speech)

If the original speaker's present and future is still present and future, the tense remains unchanged.

7. In case of modal verbs

can becomes could

will - would

Shall - should

may - might

would, should, could, might, ought to and must are unchanged.

Nagesh said, 'I can solve this sum.' (direct speech)

Nagesh said that he could solve that sum. (indirect speech)

Reported Speech Class 10 Solved Examples Exercises for CBSE Board

Read the dialogue given below and then complete the passage that follows.

Question 1.

Read the dialogue and complete the passage given below.

Interviewer: So, why do you want to be a computer programmer?

Ravi: Well, I have always been interested in computers.

Interviewer: I see. Do you have any experience?

Ravi: No, but I'm a fast learner.

Interviewer: What kind of a computer do you use?

Ravi: Computer? Uhm, let me see. I can use a Mac. I also used Windows 10 once.

Interviewer: That's good.

Ravi recently attended an interview for the selection of a computer programmer. At the interview, he was asked (a).......... To this question he replied that he wanted to change his job because (b).

When the interviewer asked him (e) ............................... he replied that. h... (..)........................................................................................ Finally, the interviewer wanted to know. (..)...............................................................................avi. replied that he could use a Mac and had also used Windows 10 once in the,.past. The interviewer seemed to be pleased with his answers.

(a) why he wanted to be a computer programmer

(b) he had always been interested in computers

(d) didn't but that he was a fast learner

(e) the kind of computer he used

Question 2.

Manu: Where are you going to?

Annu: I am going to the market. Do you want anything?

Manu asked Annu (a)..........................

(a) where she was going.

(b) that she was going to the market

Question 3.

Annu replied (b).... Annu replied (b). ............ and she further asked (C)..........

Sunita: Tomorrow is your birthday, what do you want as a gift?

Neetu: That is a lovely thought but I don't want anything.

Sunita asked Neetu since the next day was her birthday, (a).....Neetu replied that (b)...but (C).....

(a) what she wanted as a gift

(b) that was a lovely thought

Question 4.

Gardener: Did you water the plant today?

Dev: No, but I will, today.

Gardener: Then tomorrow I will get a sapling of sunflower.

The Gardener asked Dev (a)

Dev replied negatively but (b)

Then the gardener said that (c)

(a) if/whether he had watered the plant that day.

(b) said he would that day.

Question 5.

Mr. Harish: Can you polish my shoes?

Cobbler: Yes sir. But I will take 10 for each shoe.

Mr. Harish: I will not mind as long as it is done. Mr. Harish asked the cobbler (a) .................. The cobbler replied affirmatively but (b).............. Mr. Harish said that (C)...

(a) if/whether he could polish his shoes.

(b) said that he would take 10 for each shoe

Question 6.

Electrician: When did your electricity go?

Mohan: It is not working since evening.

Electrician: Sorry sir, in this case, I will have to check the fuse now.

The electrician asked Mohan (a)........................................Mohan replied that(b)....................................The electrician apologetically said that in that case (c )…………………………………………….

(a) when his electricity had gone.

(b) it was not working since evening.

Question 7.

Teacher Children, let us all pledge to save trees.

Children: Yes, mam, we all pledge to save our trees as the trees are the lungs of the city. Teacher: Let us start today by planting a sapling.

The teacher asked all the children to pledge to save trees. The children replied affirmatively (a)...............as the (b).......................Then the teacher said that said that (c)...........

(a) saying that they all pledged to save trees

(b) trees are the lungs of the city.

Question 8.

Buddha: Honesty is the best policy.

Disciple: Does honesty always pay?

Buddha : It may or may not, but at least you will never feel guilty.

Buddha in his preaching said that (a).......................the best policy. A disciple asked him if (b)..................always pays, Buddha replied (C )…………………………..but at least he would never feel guilty.

(a) Honesty is

(b) honesty

Question 9.

Doctor: You should take this medicine every day.

Patient: Should I take it before dinner or after dinner?

Doctor: No, you should take it after breakfast.

The Doctor advised the patient that (a).....................The patient further asked (b).....................The doctor replied negatively and then said ©……………………..

(a) he should take that medicine every day.

(b) if/whether he should take it before dinner or after dinner.

Question 10.

Reena: Do you know how to swim?

Surbhi : Yes I know. I have learnt it during this summer vacation.

Reena asked Surbhi (a)...........Then Surbhi replied (b).................and also added that (c)....................

(a) if/whether she knew how to swim

(b) in affirmative

Why Should You Download Direct and Indirect Speech Exercises for Class 10 with Answers Free PDF ?

Feeling lost in the world of "said" and "that"? Does converting direct speech to indirect speech leave you scratching your head? Worry no more! Here's your chance to download a free PDF packed with Reported Speech Exercises for Class 10 with Answers specifically designed for Class 10 students. Master this essential grammar concept and boost your confidence for exams and beyond!

If you want the free direct and indirect speech exercises for Class 10 PDF, visit Vedantu’s website, find the chapter and click on the download button.

The Reported Speech Exercises for Class 10 with Answers free PDFs available at Vedantu are easy to access and are also convenient, secure and compact.

The Direct and Indirect Speech Exercises for Class 10 with Answers PDF are completely reliable to practice for exams as they have been curated by the subject matter experts based on the latest syllabus.

The Vedantu’s teachers have given all the rules and directions for converting direct to indirect speeches with many examples. Several rules for converting direct speech to indirect speech need to be practised repeatedly, and the exercises from Vedantu's end will help you with that. Download direct and indirect speech exercises for class 10 pdf with answers and practise the solved exercises to ensure firm grip of the topic and solve your exam questions with ease. You can also sign up for our online classes to improve your hold on English grammar and fetch excellent results.

FAQs on Direct and Indirect Speech Class 10 CBSE English

1. What is the alternative name for direct speech?

Direct speeches are also called quoted speeches as the speaker's statements are provided in an exact manner , word-by-word, and are always within quotation marks.

2. How many parts does a direct speech have?

A direct speech is generally made up of two parts: reporting clause (verbs like say/said, ask/asked, shout/shouted, etc.) and the reported clause (the original dialogue of the speaker).

3. What is the indirect speech form of the word 'tomorrow'?

The word 'tomorrow' in a direct speech changes to 'the following day' or 'the next day’ in the indirect or reported speech.

4. What is Direct Speech in ?

Direct speech is the exact words spoken by someone, enclosed in quotation marks. It represents the speaker's original words and is commonly used in dialogue or reporting speech directly.

5. What is Indirect Speech?

Indirect speech, also known as reported speech, is the reporting of someone's words without using their exact words. It does not require quotation marks and often involves transforming the original speaker's words into a different form.

6. Why is it important to learn direct and indirect speech exercises for class 10 pdf with answers?

Learning direct and indirect speech exercises for class 10 pdf with answers is essential in Class 10 CBSE English Grammar as it enhances students' comprehension skills, improves their writing ability, and enables them to effectively report speech in various contexts, such as narratives, essays, and dialogue-based questions in exams.

7. What are the basic rules for transforming Direct Speech into Indirect Speech?

The basic rules for transforming direct speech into indirect speech include changing verb tenses, pronouns, time and place references, and often using reporting verbs such as 'said,' 'told,' or 'asked.'

8. How can I practice Direct and Indirect Speech effectively?

Practicing direct and indirect speech involves analyzing sentences, identifying the reporting verbs, and applying the appropriate rules for transforming direct speech into indirect speech. Engaging in exercises and writing prompts, as provided in resources like the Class 10 CBSE English Grammar PDF, can enhance proficiency.

9. Is the direct and indirect speech class 10 CBSE English Grammar PDF a reliable resource for learning Direct and Indirect Speech?

Yes, the direct and indirect speech class 10 PDF is a reputable resource provided by the Central Board of Secondary Education (CBSE) and is designed to align with the curriculum standards. It typically includes explanations, examples, and exercises covering various grammar topics, including direct and indirect speech.

10. Are there any tips for mastering Reported Speech Class 10 CBSE English Grammar exams?

Some tips for Reported Speech Class 10 include practicing regularly, paying attention to verb tense changes, ensuring consistency in pronoun usage, and understanding the context of reported speech. Additionally, seeking clarification from teachers or referring to supplementary study materials can aid in comprehension and application.

11. Can I find additional Reported Speech Exercises for Class 10 with Answers online?

Yes, Vedantu offers additional Reported Speech Exercises for Class 10 with Answers and explanations for direct and indirect speech. These resources can complement the Class 10 CBSE English Grammar PDF and provide further opportunities for practice and reinforcement.

Reported Speech

Reported Speech: Whenever you are quoting someone else’s words , you use two kinds of speeches – Direct or Indirect speech . In this chapter, we will learn all about Direct and Indirect speech and how to convert one into another.

Browse more Topics under Transformation Sentences

Active and Passive Voice
Parts of Speech
Types of Sentences

Reported speech- Play of the tenses:

Learn more about Parts of Speech here in detail

This is a summary table that will be crystal clear to you as you read further. Just come back to this table after this section and use this as a summary table:

Some word transitions from direct to reported speech that will come in handy:

Will becomes would
Can becomes could
would stays would
should stays should
must stays must or had to(matter of choice)
shall becomes should

Exception : A present tense in direct speech may not become a past tense in the reported speech if it’s a fact or something generic we are talking about in the sentence. For example-

Direct speech: The sun rises from the East.

Reported speech: She said that the sun rises/rose from the East.

Reported speech- Handling questions:

What happens when the sentence we are trying to report was actually a question? That’s something we are going to deal with in this section. Reported questions- It’s quite interesting. let’s get into it:

Well the good news is that the tense change you learnt above stays the same in reported speech for questions. The only difference is that when you report a question, you no more report it in the form of a question but in the form of a statement. For example:

Direct speech: Where do you want to eat?

Reported speech: She asked me where I wanted to eat.

Notice how the question mark is gone from the reported speech. The reported speech is a statement now. Keep that in mind as you read further.

Remember the tense change? Let’s apply that to a few questions now.

Now these are questions that have wordy answers to them. What about the questions that has yes/no answers to them? In these type of questions just add “if” before asking the question. For example:

Direct speech: Would you like to eat some cupcakes?
Reported speech: He asked me if i would like to eat some cupcakes.
Direct speech: Have you ever seen the Van Gogh paintings?
Reported speech: She asked me if I had ever seen the Van Gogh paintings.
Direct speech: Are you eating your vegetables?
Reported speech: She asked if I was eating my vegetables.

Reported speech- Reported requests:

Well not all questions require answers. Some questions are polite requests. Remember? Could you please try to remember? And then there are request statements. Let’s see how do we convert these into reported speech.

Reported request = ask me + to + verb or requested me + to +verb

Just add this rule to your reported speech and you have what is called a reported request.

Reported speech- Reported orders:

Well, not everyone is going to be polite. Sometimes, we get orders. Now how will you report them? Unlike the request, the reporting verb isn’t ask but told or tell. Also, when in orders, sometimes subjects are omitted but while reporting we have to revive the subjects. Let’s see a few examples:

Direct speech: Sit down!
Reported speech: She told me to sit down.
Direct speech: don’t worry!
Reported speech: She told me not to worry.

Reported speech- Time transitions:

With that, you have everything it takes to understand reported speech. you are all se to change the direct to reported speech. Go ahead and try a few examples. All the best!

Customize your course in 30 seconds

Which class are you in.

Transformation of Sentences

Active and Passive voice

37 responses to “Active and Passive voice”

Simple but very nice explanation and helpfull too.

What is the voice change of ” I have endeavoured to understand the fundamental truths.”

ENDEAVOUR HAS BEEN MADE BY ME TO UNDERSTAND THE FUNDAMENTAL TRUTH.

The fundamental truths have been endeavoured to be understood by me

The fundamental truths to understand had been endeavoured by him

The fundamental truths have endeavoured to be understood by me

The fundamental truths has been understood endeavoured to by me

How to change the voice for the following sentence – the books will be received by tomorrow

By whom? We need a subject. If the subject was for example “The library”, then the sentence in active voice would read “The library will receive the books by tomorrow”.

You will receive the books by tomorrow.

Tomorrow you will receive the book

You will receive the books (by) tomorrow.

Someone will receive the books by tomorrow

Tomorrow will be receive the books

HE WILL RECEIVE THE BOOKS BY TOMORROW.

By tomorrow the books will be received.

By tomorrow, you will receive the books

Tomorrow received the book

Change this “take right and turn left” into passive voice

Let the right be taken amd left be turned

‘amd’ is “and” 😅

You are advised to take right and turn left

Very helpful information thanks

Very well explained all basics that can lead to gain further knowledge very easily

What is in this box change into passive

what is the voice change of,” some people think nuclear is the best, because it doesnt add to global warming “….

Brilliant stuff!! – Rishabh

A kite was made by Ravi . What is the active form of this statement???

how to change into passive this sentence “when they were shifting the patient to the I.C.U.,he died

change into passive voice this sentence “when they were shifting the patient to I.C.U.,he died .

May you tell us tense conversion in voice.

Sentences without action like…. Jim is a doctor . Is it active or passive and if any how would you decide without having a main verb ?

It is named after the name of its principal tree ‘sundari'(passive)

how can ocean be object 🙄???

They made a bag

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

Perfectyourenglish.com

English Grammar Lessons And Worksheets

Class 10 worksheets

Reported Speech Worksheet for Class 10 CBSE

by Manjusha Nambiar · Published November 30, 2023 · Updated April 7, 2024

If you want to learn about reported speech before doing this worksheet, go to the reported speech study page.

1. Rahul meets Shyam at the bus stop. Shyam was absent from school yesterday.

Rahul: Why were you absent yesterday?

Shyam: I went to see my grandmother. She has been ill for a while.

Complete the sentence by reporting the question and the reply correctly.

While meeting Shyam at the bus stop, Rahul asked why he …………………………………….. to which Shyam replied that ……………………………….

Rahul asked why he had been absent the previous day to which Shyam replied that he had gone to meet his grandmother who had been ill for a while.

2. Rani is speaking to her hairdresser.

Hairdresser: Hi Rani, what can I do for you?

Rani: Well, I would like a nice short haircut for the summer.

Report the conversation by completing the sentence.

The hairdresser asked Rani ……………………………………………. Rani replied that ……………………………………………

The hairdresser asked Rani what she could do for her . Rani replied that she would like a nice short haircut for the summer.

3. Meera can’t find her kitten. He has been missing since morning. She goes to her neighbours’ house and asks them if they had seen her kitten.

Meera: Have you seen my cat? He has been missing since morning.

Neighbour: No, I haven’t but I will let you know if I find him.

Meera asked her neighbor ……………………………………………………….. who ……………………………………………. Her neighbour replied that ………………………………………………….. but she ……………………………………………………….

Meera asked her neighbour if/whether she had seen her cat who had been missing since morning . Her neighbour replied that she hadn’t seen the cat but she would let her know if she found him.

4. Mother: Ammu, did you eat your lunch?

Ammu: No. I wasn’t hungry.

Report this conversation.

Mother asked Ammu ………………………………………………….. to which Ammu replied that ………………………………………… because ………………………………………………

Mother asked Ammu if / whether she had eaten her lunch to which Ammu replied that she hadn’t because she was not hungry.

5. Rani would like to go to the movies. She asks her friend Priyanka if she would like to come with her.

Rani: I would like to watch a movie tonight. Would you like to come with me?

Priyanka: I would like to but I can’t. I have an important assignment to complete.

Rani told Priyanka that she would like to watch a movie and asked her …………………………………………………….. Priyanka replied that ……………………………………………………. because ……………………………………………………….

Rani told Priyanka that she would like to watch a movie and asked her if she would like to go with her. Priyanka replied that she would like to but she couldn’t because she had an important assignment to complete.

6. Gauri: Mummy, I can’t find my calculator.

Mother: Where did you leave it?

Gauri: I left it on the table.

Mother: Ask Ravi if he has taken it.

Gauri told her mother that ………………………………………………………. to which mother asked ………………………………………………….. Gauri replied that she ………………………………………………… and then mother told her ……………………………………………………………..

Gauri told her mother that she couldn’t find her calculator to which her mother asked where she had left it. Gauri replied that she had left it on the table and then mother told her to ask Ravi if he had taken it.

Grammar worksheet for classes 9 and 10 | Omission
Time expressions used with simple present tense
Non-finite verbs worksheet for class 10
Preposition worksheet for CBSE Class 9
Relative pronouns worksheet for CBSE Class 10
Dialogue completion exercise for Class 10
Prepositions worksheet for CBSE Class 10

Reported Speech Worksheet | Dialogue Completion Worksheet For Class 10
Reported Speech Worksheet for Class 9 CBSE
Reported Speech Worksheet For Classes 9 And 10
Reported Speech Worksheet For Class 10 | Reporting Questions
Reported Speech Worksheet For Class 10
Direct And Indirect Speech Worksheet For Class 8 CBSE
Direct and Indirect Speech Worksheet for Class 10 CBSE
Reported Speech Worksheet for Class 10 KSEEB SSLC

Tags: class 10 reported speech worksheet reported speech worksheet for class 10

Manjusha Nambiar

Hi, I am Manjusha. This is my blog where I give English grammar lessons and worksheets.

Next story Phrasal Verbs Exercise for Class 10 CBSE
Previous story Phrase Worksheet for Class 8

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Explore perfectyourenglish.com

Useful links

English Grammar
Business English
A word a day
B2 First (Cambridge English)
Bank PO English
CBSE Grammar Worksheets
Class 11 Worksheets
Class 2 Worksheets
Class 3 worksheets
Class 4 worksheets
Class 5 worksheets
Class 6 worksheets
Class 7 worksheets
Class 8 worksheets
Class 9 and 10 worksheets
Class 9 worksheets
Grammar exercises
Karnataka Board English
Kerala Syllabus English
Maharashtra Syllabus English
Phrasal verbs
PSC Coaching
Reading comprehension
Sample Letters
Tamil Nadu Board English
Test Preparation
Uncategorized
Vocabulary exercises

Never Miss an Update!

Enter your email address to receive our lessons in your inbox:

Delivered by FeedBurner

Sites in our network

NCERT Guides

Learn Maths Online | Free NCERT Maths Solutions

Reported Speech Worksheet For Class 10 CBSE

by Manjusha · Published May 16, 2020 · Updated March 23, 2022

Read the piece of conversation given below and fill in the blanks.

Chiki: Have you bought a birthday gift for Pooja? Miki: Yes, but I don’t know when and how to give it. Chiki: Hello, I don’t get you. Miki: I mean, I am new to such functions. Chiki asked Miki ………(a)…………… Miki ……….(b)………… A surprised Chiki commented ………….(c)……….. Miki defended him by saying ………(d)…………….

Chiki asked Miki if he had bought a birthday gift for Pooja . Miki said Yes and added that he didn’t know when and how to give it . A surprised Chiki commented that she didn’t get him . Miki defended him by saying that he was new to such functions .

More direct and indirect speech worksheets

Direct and indirect speech worksheet for class 10
Direct and indirect speech worksheet | Reporting yes/no questions

Reporting Wh-questions

Related worksheets

Direct and indirect speech worksheet for class 7
Direct and indirect speech worksheet for classes 7 and 8
Changing yes/no questions from direct speech to indirect speech
Reported speech worksheet for class 7
Direct and indirect speech worksheet for class 8
Direct and indirect speech worksheet for class 6

Tags: class 10 grammar worksheets direct and indirect speech worksheet english grammar worksheets reported speech worksheet

Next story Class 10 Letter Writing Sample
Previous story Class 10 Grammar Worksheet | Complete The Given Passage

Phrasal Verbs for Class 10

January 14, 2024

by Manjusha · Published January 14, 2024 · Last modified March 25, 2024

Class 10 English Pre-Board Exam Question Paper

January 13, 2024

by Manjusha · Published January 13, 2024

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Biographies
Career Guidance
Class 10 NCERT Solutions
Class 2 Solutions
Class 3 Solutions
Class 4 Solutions
Class 5 Solutions
Class 6 NCERT Solutions
Class 7 Solutions
Class 8 NCERT Solutions
Class 9 NCERT Solutions
English Grammar
English Grammar Worksheets
English Vocabulary Exercises
General Knowledge
Inspirational Stories
Kerala Syllabus English
KSEEB Solutions
Personality Development
Political Science
Sample Question Papers
Social Science
Social science
State Boards
Tamil Nadu Board
Uncategorized
West Bengal Board
Writing Skills
Underline the Proper Nouns Worksheet for Class 2
Reported Speech Exercise for Class 10 TN Board
List of Plural Nouns for Class 10
Suffixes Worksheet for Class 10
Synonyms Worksheet for Class 10 TN Board

Syllabus 2024-25
CBSE Class X SQP 2023-24
CBSE Class XII SQP 2023-24
Class X SQP 2022-23
Class XII SQP 2022-23
Request Answers

Reported Speech: Commands and Requests Practice Exercises

Post last modified: 10 April 2022
Post category: Grammar Exercises / School Grammar

Learn converting commands and request type Imperative sentences into Indirect Speech or narration. The solved exercises given below are here to do practice on these exercises. Attempt yourself first and then see the answers.

New exercises are added from time to time, so, keep coming here.

Narration: Commands and Requests

Q. change the following sentences into indirect speech..

He said to his servant, “Leave the room at once”.
He said to him, “Please wait here till I return.”
Sara’s mother said to her, “Cook the food properly”.
The teacher said to a student, “Don’t waste your time”.
The police man shouted to the man, “Stop or I will shoot you”.
My elder brother said to me, “Please post this letter for me”.
I said to my brother, “Let us go to some hill station for a change”.
The police officer said to a culprit, “Don’t try to be clever”.
The judge said to the accused, “Hold your tongue”.
He shouted, “Let me go.”
She said, “Be quiet and listen to his words.”
I said to my teacher, ” Pardon me sir”
He ordered the servant to leave the room at once.
He requested him to wait there till he returned.
Sara’s mother ordered her to cook the food properly.
The teacher ordered a student not to waste the time.
The police man ordered the man to stop and threatened that otherwise he would shoot him.
My elder brother requested me to post this letter for him.
I suggested to my brother that we should go to some hill station for a change.
The police officer ordered a culprit not to try to be clever.
The judge ordered the accused to hold his tongue.
He shouted to let him go.
He urged them to be quiet and listen to his work.
I respectfully begged my teacher to pardon me.

want to share! Share this content

Opens in a new window

Subject verb concord practice questions for cbse/icse schools, story writing class 10 english grammar, reading comprehension passages english grammar cbse/icse, prepositions: practice exercises english grammar, leave a reply cancel reply.

Reported speech - 1

Reported speech - 2

Reported speech - 3

Worksheets - handouts

Reported speech

Worksheets - pdf exercises.

Reported statements - worksheet
Worksheet - reported questions
Reported yes/no questions
Worksheet - reported speech
Reported speech - exercises pdf
Indirect speech - exercises
Reported speech - exercises
Mixed reported speech 1
Mixed reported speech 2
Reported speech 1
Reported speech 2
Reported speech 3
Reported speech 4
Reported speech 5
Reported wh- questions
Reported speech - worksheet
Reported commands
Reported questions
Reported speech 1
Reported speech 2
Reported requests and orders
Reported speech exercise
Reported questions - worksheet
Indirect speech - worksheet
Worksheets pdf - print
Grammar worksheets - handouts

Grammar - lessons

Reported speech - grammar notes
How to use reported speech - lesson
Tense changes - grammar

Skip to main content
Keyboard shortcuts for audio player

All Things Considered

Latest Show
Consider This Podcast
About The Program
Contact The Program
Corrections

Listen to the lead story from this episode.

Hope Hicks testifies in Trump trial

by Ximena Bustillo

Middle East

Pressure mounts for netanyahu.

by Peter Kenyon

A conversation with the Derby's 1st Black trainer in over 30 years

A "Vote Here Tuesday" sign is seen in Burlington, Vt., in 2020. In 2023, the city voted to allow non-U.S. citizens who are in the country legally to vote in local elections. But their turnout remains low. Alex Wong/Getty Images hide caption

Some cities allow noncitizens to vote in local elections. Their turnout is quite low

by Mikaela Lefrak

United Methodist Church lifts bans on LGBTQ clergy and same-sex weddings

by Jason DeRose

'Marfa for Beginners' podcast welcomes you to the art town

Florida mom runs world-record mile while pushing a stroller.

Searching for a song you heard between stories? We've retired music buttons on these pages. Learn more here.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
My Account Login
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 26 April 2024

Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS

Miguel Angrick 1 ,
Shiyu Luo 2 ,
Qinwan Rabbani 3 ,
Daniel N. Candrea 2 ,
Samyak Shah 1 ,
Griffin W. Milsap 4 ,
William S. Anderson 5 ,
Chad R. Gordon 5 , 6 ,
Kathryn R. Rosenblatt 1 , 7 ,
Lora Clawson 1 ,
Donna C. Tippett 1 , 8 , 9 ,
Nicholas Maragakis 1 ,
Francesco V. Tenore 4 ,
Matthew S. Fifer 4 ,
Hynek Hermansky 10 , 11 ,
Nick F. Ramsey 12 &
Nathan E. Crone 1

Scientific Reports volume 14 , Article number: 9617 ( 2024 ) Cite this article

592 Accesses

14 Altmetric

Metrics details

Amyotrophic lateral sclerosis
Neuroscience

Brain–computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant’s voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.

A high-performance speech neuroprosthesis

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity

Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis

Introduction.

A variety of neurological disorders, including amyotrophic lateral sclerosis (ALS), can severely affect speech production and other purposeful movements while sparing cognition. This can result in varying degrees of communication impairments, including Locked-In Syndrome (LIS) 1 , 2 , in which patients can only answer yes/no questions or select from sequentially presented options using eyeblinks, eye movements, or other residual movements. Individuals such as these may use augmentative and alternative technologies (AAT) to select among options on a communication board, but this communication can be slow, effortful, and may require caregiver intervention. Recent advances in implantable brain-computer interfaces (BCIs) have demonstrated the feasibility of establishing and maintaining communication using a variety of direct brain control strategies that bypass weak muscles, for example to control a switch scanner 3 , 4 , a computer cursor 5 , to write letters 6 or to spell words using a hybrid approach of eye-tracking and attempted movement detection 7 . However, these communication modalities are still slower, more effortful, and less intuitive than speech-based BCI control 8 .

Recent studies have also explored the feasibility of decoding attempted speech from brain activity, outputting text or even acoustic speech, which could potentially carry more linguistic information such as intonation and prosody. Previous studies have reconstructed acoustic speech in offline analysis from linear regression models 9 , convolutional 10 and recurrent neural networks 11 , 12 , and encoder-decoder architectures 13 . Concatenative approaches from the text-to-speech synthesis domain have also been explored 14 , 15 , and voice activity has been identified in electrocorticographic (ECoG) 16 and stereotactic EEG recordings 17 . Moreover, speech decoding has been performed at the level of American English phonemes 18 , spoken vowels 19 , 20 , spoken words 21 and articulatory gestures 22 , 23 .

Until now, brain-to-speech decoding has primarily been reported in individuals with unimpaired speech, such as patients temporarily implanted with intracranial electrodes for epilepsy surgery. To date, it is unclear to what extent these findings will ultimately translate to individuals with motor speech impairments, as in ALS and other neurological disorders. Recent studies have demonstrated how neural activity acquired from an ECoG grid 24 or from microelectrodes 25 can be used to recover text from a patient with anarthria due to a brainstem stroke, or from a patient with dysarthria due to ALS, respectively. Prior to these studies, a landmark study allowed a locked-in volunteer to control a real-time synthesizer generating vowel sounds 26 . More recently, Metzger et al. 27 demonstrated in a clinical trial participant diagnosed with quadriplegia and anarthria a multimodal speech-neuroprosthetic system that was capable of synthesizing sentences in a cued setting from silent speech attempts. In our prior work, we presented a ‘plug-and-play’ system that allowed a clinical trial participant living with ALS to issue commands to external devices, such as a communication board, by using speech as a control mechanism 28 .

In related work, BCIs based on non-invasive modalities, such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) or functional magnetic resonance imaging (fMRI) have been investigated for speech decoding applications. These studies have largely focused on imagined speech 29 to avoid contamination by movement artifacts 30 . Recent work by Dash et al., for example, reported speech decoding results for imagined and spoken phrases from 3 ALS patients using magnetoencephalography (MEG) 31 . While speech decoding based on non-invasive methodologies is an important branch in the BCI field as they do not require a surgery and may be adopted by a larger population more easily, their current state of the art comes with disadvantages compared to implantable BCI’s as they lack either temporal or spatial resolution, or are currently not feasible for being used at home.

Here, we show that an individual living with ALS and participating in a clinical trial of an implantable BCI (ClinicalTrials.gov, NCT03567213) was able to produce audible, intelligible words that closely resembled his own voice, spoken at his own pace. Speech synthesis was accomplished through online decoding of ECoG signals generated during overt speech production from cortical regions previously shown to represent articulation and phonation, following similar previous work 11 , 19 , 32 , 33 . Our participant had considerable impairments in articulation and phonation. He was still able to produce some words that were intelligible when spoken in isolation, but his sentences were often unintelligible. Here, we focused on a closed vocabulary of 6 keywords, originally used for decoding spoken commands to control a communication board. Our participant was capable of producing these 6 keywords individually with a high degree of intelligibility. We acquired training data over a period of 6 weeks and deployed the speech synthesis BCI in several separate closed-loop sessions. Since the participant could still produce speech, we were able to easily and reliably time-align the individual’s neural and acoustic signals to enable a mapping between his cortical activity during overt speech production processes and his voice’s acoustic features. We chose to provide delayed rather than simultaneous auditory feedback in anticipation of ongoing deterioration in the patient’s speech due to ALS, with increasing discordance and interference between actual and BCI-synthesized speech. This design choice would be ideal for a neuroprosthetic device that remains capable of producing intelligible words as an individual’s speech becomes increasingly unintelligible, as was expected in our participant due to ALS.

Here, we present a self-paced BCI that translates brain activity directly to acoustic speech that resembles characteristics of the user’s voice profile, with most synthesized words of sufficient intelligibility to be correctly recognized by human listeners. This work makes an important step in adding more evidence that recent speech synthesis from neural signals in patients with intact speech can be translated to individuals with neurological speech impairments, by first focusing on a closed vocabulary that the participant can reliably generate at his own pace, before generalizing towards unseen words. Synthesizing speech from the neural activity associated with overt speech allowed us to demonstrate the feasibility of reproducing the acoustic features of speech when ground truth is available and its alignment with an acoustic target is straightforward, in turn setting a standard for future efforts when ground truth is unavailable, as in the Locked In Syndrome. Moreover, because our speech synthesis model was trained on data that preceded testing by several months, our results also support the stability of ECoG as a basis for speech BCIs.

In order to synthesize acoustic speech from neural signals, we designed a pipeline that consisted of three recurrent neural networks (RNNs) to (1) identify and buffer speech-related neural activity, (2) transform sequences of speech-related neural activity into an intermediate acoustic representation, and (3) eventually recover the acoustic waveform using a vocoder. Figure 1 shows a schematic overview of our approach. We acquired ECoG signals from two electrode grids that covered cortical representations for speech production including ventral sensorimotor cortex and the dorsal laryngeal area (Fig. 1 A). Here, we focused only on a subset of electrodes that had previously been identified as showing significant changes in high-gamma activity associated with overt speech production (see Supplementary Fig. 2 ). From the raw ECoG signals, our closed-loop speech synthesizer extracted broadband high-gamma power features (70–170 Hz) that had previously been demonstrated to encode speech-related information useful for decoding speech (Fig. 1 B) 10 , 14 .

Overview of the closed-loop speech synthesizer. ( A ) Neural activity is acquired from a subset of 64 electrodes (highlighted in orange) from two 8 × 8 ECoG electrode arrays covering sensorimotor areas for face and tongue, and for upper limb regions. ( B ) The closed-loop speech synthesizer extracts high-gamma features to reveal speech-related neural correlates of attempted speech production and propagates each frame to a neural voice activity detection (nVAD) model ( C ) that identifies and extracts speech segments ( D ). When the participant finishes speaking a word, the nVAD model forwards the high-gamma activity of the whole extracted sequence to a bidirectional decoding model ( E ) which estimates acoustic features ( F ) that can be transformed into an acoustic speech signal. ( G ) The synthesized speech is played back as acoustic feedback.

We used a unidirectional RNN to identify and buffer sequences of high-gamma activity frames and extract speech segments (Fig. 1 C,D). This neural voice activity detection (nVAD) model internally employed a strategy to correct misclassified frames based on each frame's temporal context, and additionally included a context window of 0.5 s to allow for smoother transitions between speech and non-speech frames. Each buffered sequence was forwarded to a bidirectional decoding model that mapped high-gamma features onto 18 Bark-scale cepstral coefficients 34 and 2 pitch parameters, henceforth referred to as LPC coefficients 35 , 36 (Fig. 1 E,F). We used a bidirectional architecture to include past and future information while making frame-wise predictions. Estimated LPC coefficients were transformed into an acoustic speech signal using the LPCNet vocoder 36 and played back as delayed auditory feedback (Fig. 1 G).

Synthesis performance

When deployed in sessions with the participant for online decoding, our speech-synthesis BCI was reliably capable of producing acoustic speech that captured many details and characteristics of the voice and pacing of the participant’s natural speech, often yielding a close resemblance to the words spoken in isolation from the participant. Figure 2 A provides examples of original and synthesized waveforms for a representative selection of words time-aligned by subtracting the duration of the extracted speech segment from the nVAD. Onset timings from the reconstructed waveforms indicate that the decoding model captured the flow of the spoken word while also synthesizing silence around utterances for smoother transitions. A comparison between voice activity for spoken and synthesized speech revealed a median Levenstein distance of 235 ms, hinting that the synthesis approach was capable of generating speech that adequately matched the timing of the spoken counterpart. Figure 2 B shows the corresponding acoustic spectrograms for the spoken and synthesized words, respectively. The spectral structures of the original and synthesized speech shared many common characteristics and achieved average correlation scores of 0.67 (± 0.18 standard deviation) suggesting that phoneme and formant-specific information were preserved.

Evaluation of the synthesized words. ( A ) Visual example of time-aligned original and reconstructed acoustic speech waveforms and their spectral representations ( B ) for 6 words that were recorded during one of the closed-loop sessions. Speech spectrograms are shown between 100 and 8000 Hz with a logarithmic frequency range to emphasize formant frequencies. ( C ) The confusion matrix between human listeners and ground truth. ( D ) Distribution of accuracy scores from all who performed the listening test for the synthesized speech samples. Dashed line shows chance performance (16.7%).

We conducted 3 sessions across 3 different days (approximately 5 and a half months after the training data was acquired, each session lasted 6 min) to repeat the experiment with acoustic feedback from the BCI to the participant (see Supplementary Video 1 for an excerpt). Other experiment parameters were not changed. All synthesized words were played back on loudspeakers while simultaneously recorded for evaluation.

To assess the intelligibility of the synthesized words, we conducted listening tests in which human listeners played back individual samples of the synthesized words and selected the word that most closely resembled each sample. Additionally, we mixed in samples that contained the originally spoken words. This allowed us to assess the quality of the participant’s natural speech. We recruited a cohort of 21 native English speakers to listen to all samples that were produced during our 3 closed-loop sessions. Out of 180 samples, we excluded 2 words because the nVAD model did not detect speech activity and therefore no speech output was produced by the decoding model. We also excluded a few cases where speech activity was falsely detected by the nVAD model, which resulted in synthesized silence and remained unnoticed to the participant.

Overall, human listeners achieved an accuracy score of 80%, indicating that the majority of synthesized words could be correctly and reliably recognized. Figure 2 C presents the confusion matrix regarding only the synthesized samples where the ground truth labels and human listener choices are displayed on the X- and Y-axes respectively. The confusion matrix shows that human listeners were able to recognize all but one word at very high rates. “Back” was recognized at low rates, albeit still above chance, and was most often mistaken for “Left”. This could have been due in part to the close proximity of the vowel formant frequencies for these two words. The participant’s weak tongue movements may have deemphasized the acoustic discriminability of these words, in turn resulting in the vocoder synthesizing a version of “back” that was often indistinct from “left”. In contrast, the confusion matrix also shows that human listeners were confident in distinguishing the words “Up” and “Left”. The decoder synthesized an intelligible but incorrect word in only 4% of the cases, and all listeners accurately recognized the incorrect word. Note that all keywords in the vocabulary were chosen for intuitive command and control of a computer interface, for example a communication board, and were not designed to be easily discriminable for BCI applications.

Figure 2 D summarizes individual accuracy scores from all human listeners from the listening test in a histogram. All listeners recognized between 75 and 84% of the synthesized words. All human listeners achieved accuracy scores above chance (16.7%). In contrast, when tested on the participant’s natural speech, our human listeners correctly recognized almost all samples of the 6 keywords (99.8%).

Anatomical and temporal contributions

In order to understand which cortical areas contributed to identification of speech segments, we conducted a saliency analysis 37 to reveal the underlying dynamics in high-gamma activity changes that explain the binary decisions made by our nVAD model. We utilized a method from the image processing domain 38 that queries spatial information indicating which pixels have contributed to a classification task. In our case, this method ranked individual high-gamma features over time by their influence on the predicted speech onsets (PSO). We defined the PSO as the first occurrence when the nVAD model identified spoken speech and neural data started to get buffered before being forwarded to the decoding model. The absolute values of their gradients allowed interpretations of which contributions had the highest or lowest impact on the class scores from anatomical and temporal perspectives.

The general idea is illustrated in Fig. 3 B. In a forward pass, we first estimated for each trial the PSO by propagating through each time step until the nVAD model made a positive prediction. From here, we then applied backpropagation through time to compute all gradients with respect to the model’s input high-gamma features. Relevance scores |R| were computed by taking the absolute value of each partial derivative and the maximum value across time was used as the final score for each electrode 38 . Note that we only performed backpropagation through time for each PSO, and not for whole speech segments.

Changes in high-gamma activity across motor, premotor and somatosensory cortices trigger detection of speech output. ( A ) Saliency analysis shows that changes in high-gamma activity predominantly from 300 to 100 ms prior to predicted speech onset (PSO) strongly influenced the nVAD model’s decision. Electrodes covering motor, premotor and somatosensory cortices show the impact of model decisions, while electrodes covering the dorsal laryngeal area only modestly added information to the prediction. Grey electrodes were either not used, bad channels or had no notable contributions. ( B ) Illustration of the general procedure on how relevance scores were computed. For each time step t , relevance scores were computed by backpropagation through time across all previous high-gamma frames X t . Predictions of 0 correspond to no-speech, while 1 represents speech frames. ( C ) Temporal progression of mean magnitudes of the absolute relevance score in 3 selected channels that strongly contributed to PSOs. Shaded areas reflect the standard error of the mean (N = 60). Units of the relevance scores are in 10 –3 .

Results from the saliency analysis are shown in Fig. 3 A. For each channel, we display the PSO-specific relevance scores by encoding the maximum magnitude of the influence in the size of the circles (bigger circles mean stronger influence on the predictions), and the temporal occurrence of that maximum in the respective color coding (lighter electrodes have their maximal influence on the PSO earlier). The color bar at the bottom limits the temporal influence to − 400 ms prior to PSO, consistent with previous reports about speech planning 39 and articulatory representations 19 . The saliency analysis showed that the nVAD model relied on a broad network of electrodes covering motor, premotor and somatosensory cortices whose collective changes in the high-gamma activity were relevant for identifying speech. Meanwhile, voice activity information encoded in the dorsal laryngeal area (highlighted electrodes in the upper grid in Fig. 3 A) 19 only mildly contributed to the PSO.

Figure 3 C shows relevance scores over a time period of 1 s prior to PSO for 3 selected electrodes that strongly contributed to predicting speech onsets. In conjunction with the color coding from Fig. 3 A, the temporal associations were consistent with previous studies that examined phoneme decoding over fixed window sizes of 400 ms 18 and 500 ms 40 , 41 around speech onset times, suggesting that the nVAD model benefited from neural activity during speech planning and phonological processing 39 when identifying speech onset. We hypothesize that the decline in the relevance scores after − 200 ms can be explained by the fact that voice activity information might have already been stored in the long short-term memory of the nVAD model and thus changes in neural activity beyond this time had less influence on the prediction.

Here we demonstrate the feasibility of a closed-loop BCI that is capable of online synthesis of intelligible words using intracranial recordings from the speech cortex of an ALS clinical trial participant. Recent studies 10 , 11 , 13 , 27 suggest that deep learning techniques are a viable tool to reconstruct acoustic speech from ECoG signals. We found an approach consisting of three consecutive RNN architectures that identify and transform neural speech correlates into an acoustic waveform that can be streamed over the loudspeaker as neurofeedback, resulting in an 80% intelligibility score on a closed-vocabulary, keyword reading task.

The majority of human listeners were able to correctly recognize most synthesized words. All words from the closed vocabulary were chosen for a prior study 28 that explored speech decoding for intuitive control of a communication board rather than being constructed to elicit discriminable neural activity that benefits decoder performance. The listening tests suggest that the words “Left” and “Back” were responsible for the majority of misclassified words. These words share very similar articulatory features, and our participant’s speech impairments likely made these words less discriminable in the synthesis process.

Saliency analysis showed that our nVAD approach used information encoded in the high-gamma band across predominantly motor, premotor and somatosensory cortices, while electrodes covering the dorsal laryngeal area only marginally contributed to the identification of speech onsets. In particular, neural changes previously reported to be important for speech planning and phonological processing 19 , 39 appeared to have a profound impact. Here, the analysis indicates that our nVAD model learned a proper representation of spoken speech processes, providing a connection between neural patterns learned by the model and the spatio-temporal dynamics of speech production.

Our participant was chronically implanted with 128 subdural ECoG electrodes, roughly half of which covered cortical areas where similar high-gamma responses have been reliably elicited during overt speech 18 , 19 , 40 , 42 and have been used for offline decoding and reconstruction of speech 10 , 11 . This study and others like it 24 , 27 , 43 , 44 explored the potential of ECoG-based BCIs to augment communication for individuals with motor speech impairments due to a variety of neurological disorders, including ALS and brainstem stroke. A potential advantage of ECoG for BCI is the stability of signal quality over long periods of time 45 . In a previous study of an individual with locked-in syndrome due to ALS, a fully implantable ECoG BCI with fewer electrodes provided a stable switch for a spelling application over a period of more than 3 years 46 . Similarly, Rao et al. reported robust responses for ECoG recordings over the speech-auditory cortex for two drug-resistant epilepsy patients over a period of 1.5 years 47 . More recently, we showed that the same clinical trial participant could control a communication board with ECoG decoding of self-paced speech commands over a period of 3 months without retraining or recalibration 28 . The speech synthesis approach we demonstrated here used training data from five and a half months prior to testing and produced similar results over 3 separate days of testing, with recalibration but no retraining in each session. These findings suggest that the correspondence between neural activity in ventral sensorimotor cortex and speech acoustics were not significantly changed over this time period. Although longitudinal testing over longer time periods will be needed to explicitly test this, our findings provide additional support for the stability of ECoG as a BCI signal source for speech synthesis.

Our approach used a speech synthesis model trained on neural data acquired during overt speech production. This constrains our current approach to patients with speech motor impairments in which vocalization is still possible and in which speech may still be intelligible. Given the increasing use of voice banking among people living with ALS, it may also be possible to improve the intelligibility of synthetic speech using an approach similar to ours, even in participants with unintelligible or absent speech. This speech could be utilized as a surrogate but would require careful alignment to speech attempts. Likewise, the same approach could be used with a generic voice, though this would not preserve the individual’s speech characteristics. Here our results were achieved without the added challenge of absent ground truth, but they serve as an important demonstration that if adequate alignment is achieved, direct synthesis of acoustic speech from ECoG is feasible, accurate, and stable, even in a person with dysarthria due to ALS. Nevertheless, it remains to be seen how long our approach will continue to produce intelligible speech as our patient’s neural responses and articulatory impairments change over time due to ALS. Previous studies of long-term ECoG signal stability and BCI performance in patients with more severe motor impairments suggest that this may be possible 3 , 48 .

Although our approach allowed for online, closed-loop production of synthetic speech that preserved our participant’s individual voice characteristics, the bidirectional LSTM imposed a delay in the audible feedback until after the patient spoke each word. We considered this delay to be not only acceptable, but potentially desirable, given our patient’s speech impairments and the likelihood of these impairments worsening in the future due to ALS. Although normal speakers use immediate acoustic feedback to tune their speech motor output 49 , individuals with progressive motor speech impairments are likely to reach a point at which there is a significant, and distracting, mismatch between the subject’s speech and the synthetic speech produced by the BCI. In contrast, providing acoustic feedback immediately after each utterance gives the user clear and uninterrupted output that they can use to improve subsequent speech attempts, if necessary.

While our results are promising, the approach used here did not allow for synthesis of unseen words. The bidirectional architecture of the decoding model learned variations of the neural dynamics of each word and was capable of recovering their acoustic representations from corresponding sequences of high-gamma frames. This approach did not capture more fine-grained and isolated part-of-speech units, such as syllables or phonemes. However, previous research 11 , 27 has shown that speech synthesis approaches based on bidirectional architectures can generalize to unseen elements that were not part of the training set. Future research will be needed to expand the limited vocabulary used here, and to explore to what extent similar or different approaches are able to extrapolate to words that are not in the vocabulary of the training set.

Our demonstration here builds on previous seminal studies of the cortical representations for articulation and phonation 19 , 32 , 40 in epilepsy patients implanted with similar subdural ECoG arrays for less than 30 days. These studies and others using intraoperative recordings have also supported the feasibility of producing synthetic speech from ECoG high-gamma responses 10 , 11 , 33 , but these demonstrations were based on offline analysis of ECoG signals that were previously recorded in subjects with normal speech, with the exception of the work by Metzger et al. 27 Here, a participant with impaired articulation and phonation was able to use a chronically implanted investigational device to produce acoustic speech that retained his unique voice characteristics. This was made possible through online decoding of ECoG high-gamma responses, using an algorithm trained on data collected months before. Notwithstanding the current limitations of our approach, our findings here provide a promising proof-of-concept that ECoG BCIs utilizing online speech synthesis can serve as alternative and augmentative communication devices for people living with ALS. Moreover, our findings should motivate continued research on the feasibility of using BCIs to preserve or restore vocal communication in clinical populations where this is needed.

Materials and methods

Participant.

Our participant was a male native English speaker in his 60s with ALS who was enrolled in a clinical trial (NCT03567213), approved by the Johns Hopkins University Institutional Review Board (IRB) and by the FDA (under an investigational device exemption) to test the safety and preliminary efficacy of a brain-computer interface composed of subdural electrodes and a percutaneous connection to external EEG amplifiers and computers. All experiments conducted in this study complied with all relevant guidelines and regulations, and were performed according to a clinical trial protocol approved by the Johns Hopkins IRB. Diagnosed with ALS 8 years prior to implantation, our participant’s motor impairments had chiefly affected bulbar and upper extremity muscles and had resulted in motor impairments sufficient to render continuous speech mostly unintelligible (though individual words were intelligible), and to require assistance with most activities of daily living. Our participant’s ability to carry out activities of daily living were assessed using the ALSFRS-R measure 50 , resulting in a score of 26 out of 48 possible points (speech was rated at 1 point, see Supplementary Data S5 ). Furthermore, speech intelligibility and speaking rate were evaluated by a certified speech-language pathologist, whose detailed assessment may be found in the Supplementary Note . The participant gave informed consent after being counseled about the nature of the research and implant-related risks and was implanted with the study device in July 2022. Additionally, the participant gave informed consent for use of his audio and video recordings in publications of the study results.

Study device and implantation

The study device was composed of two 8 × 8 subdural electrode grids (PMT Corporation, Chanhassen, MN) connected to a percutaneous 128-channel Neuroport pedestal (Blackrock Neurotech, Salt Lake City, UT). Both subdural grids contained platinum-iridium disc electrodes (0.76 mm thickness, 2-mm diameter exposed surface) with 4 mm center-to-center spacing and a total surface area of 12.11 cm 2 (36.6 mm × 33.1 mm).

The study device was surgically implanted during a standard awake craniotomy with a combination of local anesthesia and light sedation, without neuromuscular blockade. The device’s ECoG grids were placed on the pial surface of sensorimotor representations for speech and upper extremity movements in the left hemisphere. Careful attention was made to assure that the scalp flap incision was well away from the external pedestal. Cortical representations were targeted using anatomical landmarks from pre-operative structural (MRI) and functional imaging (fMRI), in addition to somatosensory evoked potentials measured intraoperatively. Two reference wires attached to the Neuroport pedestal were implanted in the subdural space on the outward facing surface of the subdural grids. The participant was awoken during the craniotomy to confirm proper functioning of the study device and final placement of the two subdural grids. For this purpose, the participant was asked to repeatedly speak a single word as event-related ECoG spectral responses were noted to verify optimal placement for the implanted electrodes. On the same day, the participant had a post-operative CT which was then co-registered to a pre-operative MRI to verify the anatomical locations of the two grids.

Data recording

During all training and testing sessions, the Neuroport pedestal was connected to a 128-channel NeuroPlex-E headstage that was in turn connected by a mini-HDMI cable to a NeuroPort Biopotential Signal Processor (Blackrock Neurotech, Salt Lake City, UT, USA) and external computers. We acquired neural signals at a sampling rate of 1000 Hz.

Acoustic speech was recorded through an external microphone (BETA® 58A, SHURE, Niles, IL) in a room isolated from external acoustic and electronic noise, then amplified and digitized by an external audio interface (H6-audio-recorder, Zoom Corporation, Tokyo, Japan). The acoustic speech signal was split and forwarded to: (1) an analog input of the NeuroPort Biopotential Signal Processor (NSP) to be recorded at the same frequency and in synchrony with the neural signals, and (2) the testing computer to capture high-quality (48 kHz) recordings. We applied cross-correlation to align the high-quality recordings with the synchronized audio signal from the NSP.

Experiment recordings and task design

Each recording day began with a syllable repetition task to acquire cortical activity to be used for baseline normalization. Each syllable was audibly presented through a loudspeaker, and the participant was instructed to recite the heard stimulus by repeating it aloud. Stimulus presentation lasted for 1 s, and trial duration was set randomly in the range of 2.5 s and 3.5 s with a step size of 80 ms. In the syllable repetition task, the participant was instructed to repeat 12 consonant–vowel syllables (Supplementary Table S4 ), in which each syllable was repeated 5 times. We extracted high-gamma frames from all trials to compute for each day the mean and standard deviation statistics for channel-specific normalization.

To collect data for training our nVAD and speech decoding model, we recorded ECoG during multiple blocks of a speech production task over a period of 6 weeks. During the task, the participant read aloud single words that were prompted on a computer screen, interrupted occasionally by a silence trial in which the participant was instructed to say nothing. The words came from a closed vocabulary of 6 words ("Left", "Right", "Up", "Down", "Enter", "Back", and “…” for silence) that were chosen for a separate study in which these spoken words were decoded from ECoG to control a communication board 28 . In each block, there were ten repetitions of each word (60 words in total) that appeared in a pseudo-randomized order by having a fixed set of seeds to control randomization orders. Each word was shown for 2 s per trial with an intertrial interval of 3 s. The participant was instructed to read the prompted word aloud as soon as it appeared. Because his speech was slow, effortful, and dysarthric, the participant may have sometimes used some of the intertrial interval to complete word production. However, offline analysis verified at least 1 s between the end of each spoken word and the beginning of the next trial, assuring that enough time had passed to avoid ECoG high-gamma responses leaking into subsequent trials. In each block, neural signals and audibly vocalized speech were acquired in parallel and stored to disc using BCI2000 51 .

We recorded training, validation, and test data for 10 days, and deployed our approach for synthesizing speech online five and a half months later. During the online task, the synthesized output was played to the participant while he performed the same keyword reading task as in the training sessions. The feedback from each synthesized word began after he spoke the same word, avoiding any interference with production from the acoustic feedback. The validation dataset was used for finding appropriate hyperparameters to train both nVAD and the decoding model. The test set was used to validate final model generalizability before online sessions. We also used the test set for the saliency analysis. In total, the training set was comprised of 1570 trials that aggregated to approximately 80 min of data (21.8 min are pure speech), while the validation and test set contained 70 trials each with around 3 min of data (0.9 min pure speech). The data in each of these datasets were collected on different days, so that no baseline or other statistics in the training set leaked into the validation or test set.

Signal processing and feature extraction

Neural signals were transformed into broadband high-gamma power features that have been previously reported to closely track the timing and location of cortical activation during speech and language processes 42 , 52 . In this feature extraction process, we first re-referenced all channels within each 64-contact grid to a common-average reference (CAR filtering), excluding channels with poor signal quality in any training session. Next, we selected all channels that had previously shown significant high-gamma responses during the syllable repetition task described above. This included 64 channels (Supplementary Fig. S2 , channels with blue outlines) across motor, premotor and somatosensory cortices, including the dorsal laryngeal area. From here, we applied two IIR Butterworth filters (both with filter order 8) to extract the high-gamma band in the range of 70 to 170 Hz while subsequently attenuating the first harmonic (118–122 Hz) of the line noise. For each channel, we computed logarithmic power features based on windows with a fixed length of 50 ms and a frameshift of 10 ms. To estimate speech-related increases in broadband high-gamma power, we normalized each feature by the day-specific statistics of the high-gamma power features accumulated from the syllable repetition task.

For the acoustic recordings of the participant’s speech, we downsampled the time-aligned high-quality microphone recordings from 48 to 16 kHz. From here, we padded the acoustic data by 16 ms to account for the shift introduced by the two filters on the neural data and estimated the boundaries of speech segments using an energy-based voice activity detection algorithm 53 . Likewise, we computed acoustic features in the LPC coefficient space through the encoding functionality of the LPCNet vocoder. Both voice activity detection and LPC feature encoding were configured to operate on 10 ms frameshifts to match the number of samples from the broadband high-gamma feature extraction pipeline.

Network architectures

Our proposed approach relied on three recurrent neural network architectures: (1) a unidirectional model that identified speech segments from the neural data, (2) a bidirectional model that translated sequences of speech-related high-gamma activity into corresponding sequences of LPC coefficients representing acoustic information, and (3) LPCNet 36 , which converted those LPC coefficients into an acoustic speech signal.

The network architecture of the unidirectional nVAD model was inspired by Zen et al. 54 in using a stack of two LSTM layers with 150 units each, followed by a linear fully connected output layer with two units representing speech or non-speech class target logits (Fig. 4 ). We trained the unidirectional nVAD model using truncated backpropagation through time (BPTT) 55 to keep the costs of single parameter updates manageable. We initialized this algorithm’s hyperparameters k 1 and k 2 to 50 and 100 frames of high-gamma activity, respectively, such that the unfolding procedure of the backpropagation step was limited to 100 frames (1 s) and repeated every 50 frames (500 ms). Dropout was used as a regularization method with a probability of 50% to counter overfitting effects 56 . Comparison between predicted and target labels was determined by the cross-entropy loss. We limited the network training using an early stopping mechanism that evaluated after each epoch the network performance on a held-out validation set and kept track of the best model weights by storing the model weights only when the frame-wise accuracy score was bigger than before. The learning rate of the stochastic gradient descent optimizer was dynamically adjusted in accordance with the RMSprop formula 57 with an initial learning rate of 0.001. Using this procedure, the unidirectional nVAD model was trained for 27,975 update steps, achieving a frame-wise accuracy of 93.4% on held-out validation data. The architecture of the nVAD model had 311,102 trainable weights.

System overview of the closed-loop architecture. The computational graph is designed as a directed acyclic network. Solid shapes represent ezmsg units, dotted ones represent initialization parameters. Each unit is responsible for a self-contained task and distributes their output to all its subscribers. Logger units run in separate processes to not interrupt the main processing chain for synthesizing speech.

The network architecture of the bidirectional decoding model had a very similar configuration to the unidirectional nVAD but employed a stack of bidirectional LSTM layers for sequence modelling 11 to include past and future contexts. Since the acoustic space of the LPC components was continuous, we used a linear fully connected output layer for this regression task. Figure 4 contains an illustration of the network architecture of the decoding model. In contrast to the unidirectional nVAD model, we used standard BPTT to account for both past and future contexts within each extracted segment identified as spoken speech. The architecture of the decoding model had 378,420 trainable weights and was trained for 14,130 update steps using a stochastic gradient descent optimizer. The initial learning rate was set to 0.001 and dynamically updated in accordance with the RMSProp formula. Again, we used dropout with a 50% probability and employed an early stopping mechanism that only updated model weights when the loss on the held-out validation set was lower than before.

Both the unidirectional nVAD and the bidirectional decoding model were implemented within the PyTorch framework. For LPCNet, we used the C-implementation and pretrained model weights by the original authors and communicated with the library via wrapper functions through the Cython programming language.

Closed-loop architecture

Our closed-loop architecture was built upon ezmsg, a general-purpose framework which enables the implementation of streaming systems in the form a directed acyclic network of connected units, which communicate with each other through a publish/subscribe software engineering pattern using asynchronous coroutines. Here, each unit represents a self-contained operation which receives many inputs, and optionally propagates its output to all its subscribers. A unit consists of a settings and state class for enabling initial and updatable configurations and has multiple input and output connection streams to communicate with other nodes in the network. Figure 4 shows a schematic overview of the closed-loop architecture. ECoG signals were received by connecting to BCI2000 via a custom ZeroMQ (ZMQ) networking interface that sent packages of 40 ms over the TCP/IP protocol. From here, each unit interacted with other units through an asynchronous message system that was implemented on top of a shared-memory publish-subscribe multi-processing pattern. Figure 4 shows that the closed-loop architecture was comprised of 5 units for the synthesis pipeline, while employing several additional units that acted as loggers and wrote intermediate data to disc.

In order to play back the synthesized speech during closed-loop sessions, we wrote the bytes of the raw PCM waveform to standard output (stdout) and reinterpreted them by piping them into SoX. We implemented our closed-loop architecture in Python 3.10. To keep the computational complexity manageable for this streamlined application, we implemented several functionalities, such as ringbuffers or specific calculations in the high-gamma feature extraction, in Cython.

Contamination analysis

Overt speech production can cause acoustic artifacts in electrophysiological recordings, allowing learning machines such as neural networks to rely on information that is likely to fail once deployed—a phenomenon widely known as Clever Hans 58 . We used the method proposed by Roussel et al. 59 to assess the risk that our ECoG recordings had been contaminated. This method compares correlations between neural and acoustic spectrograms to determine a contamination index which describes the average correlation of matching frequencies. This contamination index is compared to the distribution of contamination indices resulting from randomly permuting the rows and columns of the contamination matrix—allowing statistical analysis of the risk when assuming that no acoustic contamination is present.

For each recording day among the train, test and validation set, we analyzed acoustic contamination in the high-gamma frequency range. We identified 1 channel (Channel 46) in our recordings that was likely contaminated during 3 recording days (D 5 , D 6 , and D 7 ), and we corrected this channel by taking the average of high-gamma power features from neighboring channels (8-neighbour configuration, excluding the bad channel 38). A detailed report can be found in Supplementary Fig. S1 , where each histogram corresponds to the distribution of permuted contamination matrices, and colored vertical bars indicate the actual contamination index, where green and red indicate the statistical criterion threshold (green: p > 0.05, red: p ≤ 0.05). After excluding the neural data from channel 46, Roussel’s method suggested that the null hypothesis could be rejected, and thus we concluded that no acoustic speech has interfered with neural recording.

Listening test

We conducted a forced-choice listening test similar to Herff et al. 14 in which 21 native English speakers evaluated the intelligibility of the synthesized output and the originally spoken words. Listeners were asked to listen to one word at a time and select which word out of the six options most closely resembled it. Here, the listeners had the opportunity to listen to each sample many times before submitting a choice. We implemented the listening test on top of the BeaqleJS framework 60 . All words that were either spoken or synthesized during the 3 closed-loop sessions were included in the listening test, but were randomly sampled from a uniform distribution for unique randomized sequences across listeners. Supplementary Fig. S3 provides a screenshot of the interface with which the listeners were working.

All human listeners were only recruited through indirect means such as IRB-approved flyers placed on campus sites and had no direct connection to the PI. Anonymous demographic data was collected at the end of the listening test asking for age and preferred gender. Overall, recruited participants were 23.8% male and 61.9% female (14% other or preferred not to answer) ranging between 18 to 30 years old.

Statistical analysis

Original and reconstructed speech spectrograms were compared using Pearson's correlation coefficients for 80 mel-scaled spectral bins. For this, we transformed original and reconstructed waveforms into the spectral domain using the short-time Fourier transform (window size: 50 ms, frameshift: 10 ms, window function: Hanning), applied 80 triangular filters to focus only on perceptual differences for human listeners 61 , and Gaussianized the distribution of the acoustic space using the natural logarithm. Pearson correlation scores were calculated for each sample by averaging the correlation coefficients across frequency bins. The 95% confidence interval (two-sided) was used in the feature selection procedure while the z-criterion was Bonferroni corrected across time points. Lower and upper bounds for all channels and time points can be found in the supplementary data . Contamination analysis is based on permutation tests that use t-tests as their statistical criterion with a Bonferroni corrected significance level of α = 0.05/N, where N represents the number of frequency bins multiplied by the number of selected channels.

Overall, we used the SciPy stats package (version 1.10.1) for statistical evaluation, but the contamination analysis has been done in Matlab with the statistics and machine learning toolbox (version 12.4).

Data availability

Neural data and anonymized speech audio are publicly available at http://www.osf.io/49rt7/ . This includes experiment recordings used as training data and experiment runs from our closed-loop sessions. Additionally, we also included supporting data used for rendering the figures in the main text and in the supplementary material.

Code availability

Corresponding source code for the closed-loop BCI and scripts for generating figures can be obtained from the official Crone Lab Github page at: https://github.com/cronelab/delayed-speech-synthesis . This includes source files for training, inference, and data analysis/evaluation. The ezmsg framework can be obtained from https://github.com/iscoe/ezmsg .

Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the locked-in syndrome. J. Neurol. 221 , 77–91 (1979).

Article CAS PubMed Google Scholar

Smith, E. & Delargy, M. Locked-in syndrome. BMJ 330 , 406–409 (2005).

Article PubMed Central PubMed Google Scholar

Vansteensel, M. J. et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375 , 2060–2066 (2016).

Chaudhary, U. et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training. Nat. Commun. 13 , 1236 (2022).

Article ADS CAS PubMed Central PubMed Google Scholar

Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain–computer interface. eLife 6 , e18554 (2017).

Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593 , 249–254 (2021).

Oxley, T. J. et al. Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: First in-human experience. J. NeuroInterventional Surg. 13 , 102–108 (2021).

Article Google Scholar

Chang, E. F. & Anumanchipalli, G. K. Toward a speech neuroprosthesis. JAMA 323 , 413–414 (2020).

Herff, C. et al. Towards direct speech synthesis from ECoG: A pilot study. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1540–1543 (2016).

Angrick, M. et al. Speech synthesis from ECoG using densely connected 3D convolutional neural networks. J. Neural Eng. 16 , 036019 (2019).

Article ADS PubMed Central PubMed Google Scholar

Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568 , 493–498 (2019).

Wairagkar, M., Hochberg, L. R., Brandman, D. M. & Stavisky, S. D. Synthesizing speech by decoding intracortical neural activity from dorsal motor cortex. In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) 1–4 (2023).

Kohler, J. et al. Synthesizing speech from intracranial depth electrodes using an encoder-decoder framework. Neurons Behav. Data Anal. Theory https://doi.org/10.51628/001c.57524 (2022).

Herff, C. et al. Generating natural, intelligible speech from brain activity in motor, premotor, and inferior frontal cortices. Front. Neurosci. https://doi.org/10.3389/fnins.2019.01267 (2019).

Wilson, G. H. et al. Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus. J. Neural Eng. 17 , 066007 (2020).

Kanas, V. G. et al. Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals. IEEE Trans. Biomed. Eng. 61 , 1241–1250 (2014).

Soroush, P. Z., Angrick, M., Shih, J., Schultz, T. & Krusienski, D. J. Speech activity detection from stereotactic EEG. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 3402–3407 (2021).

Mugler, E. M. et al. Direct classification of all American English phonemes using signals from functional speech motor cortex. J. Neural Eng. 11 , 035015 (2014).

Bouchard, K. E., Mesgarani, N., Johnson, K. & Chang, E. F. Functional organization of human sensorimotor cortex for speech articulation. Nature 495 , 327–332 (2013).

Bouchard, K. E. & Chang, E. F. Neural decoding of spoken vowels from human sensory-motor cortex with high-density electrocorticography. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 6782–6785 (2014).

Kellis, S. et al. Decoding spoken words using local field potentials recorded from the cortical surface. J. Neural Eng. 7 , 056007 (2010).

Mugler, E. M., Goldrick, M., Rosenow, J. M., Tate, M. C. & Slutzky, M. W. Decoding of articulatory gestures during word production using speech motor and premotor cortical activity. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 5339–5342 (2015).

Mugler, E. M. et al. Differential representation of articulatory gestures and phonemes in precentral and inferior frontal gyri. J. Neurosci. 38 , 9803–9813 (2018).

Article CAS PubMed Central PubMed Google Scholar

Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385 , 217–227 (2021).

Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620 , 1031–1036 (2023).

Guenther, F. H. et al. A wireless brain–machine interface for real-time speech synthesis. PLoS ONE 4 , e8218 (2009).

Metzger, S. L. et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620 , 1037–1046 (2023).

Luo, S. et al. Stable decoding from a speech BCI enables control for an individual with ALS without recalibration for 3 months. Adv. Sci. 10 , 2304853 (2023).

Cooney, C., Folli, R. & Coyle, D. Neurolinguistics research advancing development of a direct-speech brain–computer interface. iScience 8 , 103–125 (2018).

Herff, C. & Schultz, T. Automatic speech recognition from neural signals: A focused review. Front. Neurosci. https://doi.org/10.3389/fnins.2016.00429 (2016).

Dash, D. et al. Neural Speech Decoding for Amyotrophic Lateral Sclerosis , 2782–2786 (2020). https://doi.org/10.21437/Interspeech.2020-3071 .

Chartier, J., Anumanchipalli, G. K., Johnson, K. & Chang, E. F. Encoding of articulatory kinematic trajectories in human speech sensorimotor cortex. Neuron 98 , 1042-1054.e4 (2018).

Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D. & Mesgarani, N. Towards reconstructing intelligible speech from the human auditory cortex. Sci. Rep. 9 , 874 (2019).

Moore, B. An introduction to the psychology of hearing: Sixth edition. In An Introduction to the Psychology of Hearing (Brill, 2013).

Taylor, P. Text-to-Speech Synthesis (Cambridge University Press, 2009).

Book Google Scholar

Valin, J.-M. & Skoglund, J. LPCNET: Improving neural speech synthesis through linear prediction. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 5891–5895 (2019).

Montavon, G., Samek, W. & Müller, K.-R. Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73 , 1–15 (2018).

Article MathSciNet Google Scholar

Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. In International Conference on Learning Representations (ICLR) (2014).

Indefrey, P. the spatial and temporal signatures of word production components: A critical update. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00255 (2011).

Ramsey, N. F. et al. Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids. NeuroImage 180 , 301–311 (2018).

Jiang, W., Pailla, T., Dichter, B., Chang, E. F. & Gilja, V. Decoding speech using the timing of neural signal modulation. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1532–1535 (2016).

Crone, N. E. et al. Electrocorticographic gamma activity during word production in spoken and sign language. Neurology 57 , 2045–2053 (2001).

Moses, D. A., Leonard, M. K., Makin, J. G. & Chang, E. F. Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nat. Commun. 10 , 3096 (2019).

Herff, C. et al. Brain-to-text: Decoding spoken phrases from phone representations in the brain. Front. Neurosci. https://doi.org/10.3389/fnins.2015.00217 (2015).

Morrell, M. J. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77 , 1295–1304 (2011).

Article PubMed Google Scholar

Pels, E. G. M. et al. Stability of a chronic implanted brain–computer interface in late-stage amyotrophic lateral sclerosis. Clin. Neurophysiol. 130 , 1798–1803 (2019).

Rao, V. R. et al. Chronic ambulatory electrocorticography from human speech cortex. NeuroImage 153 , 273–282 (2017).

Silversmith, D. B. et al. Plug-and-play control of a brain–computer interface through neural map stabilization. Nat. Biotechnol. 39 , 326–335 (2021).

Denes, P. B. & Pinson, E. The Speech Chain (Macmillan, 1993).

Google Scholar

Cedarbaum, J. M. et al. The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. J. Neurol. Sci. 169 , 13–21 (1999).

Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N. & Wolpaw, J. R. BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51 , 1034–1043 (2004).

Leuthardt, E. et al. Temporal evolution of gamma activity in human cortex during an overt and covert word repetition task. Front. Hum. Neurosci. https://doi.org/10.3389/fnhum.2012.00099 (2012).

Povey, D. et al. The kaldi speech recognition toolkit. In IEEE 2011 Workshop on Automatic Speech Recognition and Understanding (IEEE Signal Processing Society, 2011).

Zen, H. & Sak, H. Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 4470–4474 (2015).

Sutskever, I. Training Recurrent Neural Networks (University of Toronto, 2013).

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15 , 1929–1958 (2014).

MathSciNet Google Scholar

Ruder, S. An overview of gradient descent optimization algorithms. Preprint at https://arxiv.org/abs/1609.04747 (2016).

Lapuschkin, S. et al. Unmasking Clever Hans predictors and assessing what machines really learn. Nat. Commun. 10 , 1096 (2019).

Roussel, P. et al. Observation and assessment of acoustic contamination of electrophysiological brain signals during speech production and sound perception. J. Neural Eng. 17 , 056028 (2020).

Article ADS PubMed Google Scholar

Kraft, S. & Zölzer, U. BeaqleJS: HTML5 and JavaScript based framework for the subjective evaluation of audio quality. In Linux Audio Conference (2014).

Stevens, S. S., Volkmann, J. & Newman, E. B. A scale for the measurement of the psychological magnitude pitch. J. Acoust. Soc. Am. 8 , 185–190 (1937).

Article ADS Google Scholar

Download references

Acknowledgements

Research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number UH3NS114439 (PI N.E.C., co-PI N.F.R.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and affiliations.

Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Miguel Angrick, Samyak Shah, Kathryn R. Rosenblatt, Lora Clawson, Donna C. Tippett, Nicholas Maragakis & Nathan E. Crone

Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Shiyu Luo & Daniel N. Candrea

Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA

Qinwan Rabbani

Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA

Griffin W. Milsap, Francesco V. Tenore & Matthew S. Fifer

Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

William S. Anderson & Chad R. Gordon

Section of Neuroplastic and Reconstructive Surgery, Department of Plastic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Chad R. Gordon

Department of Anesthesiology & Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kathryn R. Rosenblatt

Department of Otolaryngology-Head and Neck Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Donna C. Tippett

Department of Physical Medicine and Rehabilitation, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

Center for Language and Speech Processing, The Johns Hopkins University, Baltimore, MD, USA

Hynek Hermansky

Human Language Technology Center of Excellence, The Johns Hopkins University, Baltimore, MD, USA

UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands

Nick F. Ramsey

You can also search for this author in PubMed Google Scholar

Contributions

M.A. and N.C. wrote the manuscript. M.A., S.L., Q.R. and D.C. analyzed the data. M.A. and S.S. conducted the listening test. S.L. collected the data. M.A. and G.M. implemented the code for the online decoder and the underlying framework. M.A. made the visualizations. W.A., C.G. and K.R., L.C. and N.M. conducted the surgery/medical procedure. D.T. made the speech and language assessment. F.T. handled the regulatory aspects. H.H. supervised the speech processing methodology. M.F. N.R. and N.C. supervised the study and the conceptualization. All authors reviewed and revised the manuscript.

Corresponding authors

Correspondence to Miguel Angrick or Nathan E. Crone .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information..

Supplementary Video 1.

Supplementary Legends.

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Angrick, M., Luo, S., Rabbani, Q. et al. Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS. Sci Rep 14 , 9617 (2024). https://doi.org/10.1038/s41598-024-60277-2

Download citation

Received : 19 October 2023

Accepted : 21 April 2024

Published : 26 April 2024

DOI : https://doi.org/10.1038/s41598-024-60277-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

IMAGES

Reported Speech: How To Use Reported Speech
Reported Speech Exercises for Class 10 CBSE With Answers
Reported Speech Dialogue Exercises For Class 10 Cbse With Answers
Reported Speech worksheet
Reported Speech: Important Grammar Rules and Examples
reported speech practice

VIDEO

Reported Speech for Class 10 CBSE Board Exam 2024
Reported Speech -Imperative Sentences (Part 3)
Reported speech board Class 🔥 English Grammar One Shot
Direct and indirect speech || Detailed explanation by Rajeev Sir
Reported speech |Class 10| English grammar
Reported Speech

COMMENTS

Reported Speech Exercises with Answers for Class 10
This article provides reported speech exercises for class 10 students. Reported Speech Exercises for Class 10 with Answers. Here is an exercise on the transformation of direct speech to indirect speech. Go through the following sentences, work them out and then check your answers to assess how far you have understood their usage.
CBSE Class 10 English Grammar
Formulae Handbook for Class 10 Maths and Science CBSE Class 10 English Grammar - Direct And Indirect Speech (Statements, Commands, Requests, and Questions) The words spoken by a person can be reported in two ways—Direct and Indirect. When we quote the exact words spoken by a person, we call it Direct Speech. Sohan said to […]
Reported Speech Exercises for Class 10 CBSE With Answers
When we use reported speech, we usually change the verbs, specific times, and pronouns. For example: Scott said that he was coming to work. He said that he would be late because there was a lot of traffic at that time. Reported Speech Exercises for Class 10 CBSE With Answers Pdf. This grammar section explains English Grammar in a clear and ...
Reported Speech: Dialogue Writing Practice Questions CBSE Class 10
Reported Speech: Dialogue Writing Practice Questions CBSE Class 10 Grammar. Reporting the narration is done two ways - Direct or Indirect. The CBSE Class 10 Gramar syllabus includes this reporting in dialogue forms. After that an exercise with blanks to be filled to transform the whole conversation in indirect form.
Reported Speech For Class 10: Exciting Exercises with Answers [PDF]
Reported Speech is an essential linguistic tool from everyday conversations to formal writing. It is important to teach reported speech to Class 10 to give them a wider scope of the English Language and vocabulary. Reported Speech is effective in conveying the thoughts and ideas of others accurately and without causing any misrepresentation.
CBSE Class 10 English Grammar
Reported Speech Exercises for Class 10 CBSE With Answers This grammar section explains English Grammar in a clear and simple way. There are example sentences to show how the language is used. NCERT Solutions for Class 10 English will help you to write better answers in your Class 10 exams. Because the Solutions are solved by subject matter experts.
Reported Speech Dialogue Exercises for Class 10 CBSE With Answers
Change No. 1. Change the reporting verb 'said 'or 'said to'into' asked' or 'inquired of'. In case of a single question, change it into 'asked' but in case of more than one question, change it into "inquired of'. Change No. 2. Use conjunction 'if' or 'whether' if the reported speech starts with a helping verb.
Reported Speech
Reported speech is used when someone says a sentence, like, "I'm going to the movie tonight". Later, we want to tell a 3rd person what the first person is doing. It works like this: We use a reporting verb i.e 'say' or 'tell'. In the present tense, just put in 'he says. Direct Speech: I like burgers.
Grammar
Assignment and Activities for CBSE Class 10 English Chapter: Reported Speech. Reporting Speech: Listen to a conversation or a speech and write a report summarizing what was said. Dialogue Rewrite: Rewrite a dialogue in reported speech, making the necessary changes in verb tense, pronouns, and time expressions. ...
Reported Speech Exercises for Class 10 CBSE With Answers
Reported Speech Questions For Class 10 Question 7. Teacher : Children, let us all pledge to save trees. Children : Yes, mam, we all pledge to save our trees as the trees are the lungs of the city. Teacher : Let us start today by planting a sapling. The teacher asked all the children to pledge to save trees.
7. Reported speech Notes NCERT Solutions for CBSE Class 10 ...
The speech of a person can be divided into two categories. Reporting verb (verb of the first clause) Reported speech ( words within inverted commas. For ex. She said, "I will be late today.". Here, First clause is 'She said' where. Said = reporting verb. Words within inverted commas are.
Direct and Indirect Speech Class 10 CBSE English
Some tips for Reported Speech Class 10 include practicing regularly, paying attention to verb tense changes, ensuring consistency in pronoun usage, and understanding the context of reported speech. Additionally, seeking clarification from teachers or referring to supplementary study materials can aid in comprehension and application.
Reported Speech: Direct and Indirect speech
Whenever you report a speech there's a reporting verb used like "say" or "tell". For example: Direct speech: I love to play football. Reported speech: She said that she loves to play football. (Note 1 : Assume a gender if not mentioned already. Note 2: Using "that" is optional.
Reported Speech Direct and Indirect Speech
🔴Click on this link to Enroll English Spoken Course - https://www.magnetbrains.com/course/spoken-english-full-video-course/' 👉Previous Video: https://www.y...
Reported Speech Exercise For Class 10
10. My father advised me not to be late and not to miss the train. 11. The teacher asked Ravi if / whether he had finished writing. 12. Sati told her friend that she had something to tell her. Exercise 2. Sentences are given in direct speech. Change them into indirect / reported speech.
Reported Speech Worksheet for Class 10 CBSE
Reported Speech Worksheet for Class 10 CBSE by Manjusha Nambiar · Published November 30, 2023 · Updated April 7, 2024 If you want to learn about reported speech before doing this worksheet, go to the reported speech study page.
Reported speech
Direct speech (exact words): Mary: Oh dear. We've been walking for hours! I'm exhausted. I don't think I can go any further. I really need to stop for a rest. Peter: Don't worry. I'm not surprised you're tired. I'm tired too. I'll tell you what, let's see if we can find a place to sit down, and then we can stop and have our picnic. Reported ...
Reported Speech
Watch This Video To Learn Reported Speech In English | Direct and Indirect Speech In English Grammar With Examples | Narration Changes/Rules - Chetna Vasisht...
Reported Speech Worksheet For Class 10 CBSE
Reported Speech Worksheet For Class 10 CBSE by Manjusha · Published May 16, 2020 · Updated March 23, 2022 Read the piece of conversation given below and fill in the blanks.
Reported Speech: Commands and Requests Practice Exercises
Grammar Exercises / School Grammar. Learn converting commands and request type Imperative sentences into Indirect Speech or narration. The solved exercises given below are here to do practice on these exercises. Attempt yourself first and then see the answers. New exercises are added from time to time, so, keep coming here.
Reported speech
Reported speech 2. Reported requests and orders. Reported speech exercise. Reported questions - worksheet. Indirect speech - worksheet. Worksheets pdf - print. Grammar worksheets - handouts. Grammar - lessons. Reported speech - grammar notes.
All Things Considered for May 4, 2024 : NPR
Florida mom runs world-record mile while pushing a stroller. less than 1 min. Audio will be available later today. Searching for a song you heard between stories? We've retired music buttons on ...
Online speech synthesis using a chronically implanted brain ...
Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for ...
Our Campus. Our Crisis
Overseeing it all was a new president, Minouche Shafik, whose inauguration had come just three days before 10/7 and who had scarcely begun to acquaint herself with the Columbia community when the campus was thrown into crisis. With national political figures and billionaires agitating for the removal of other Ivy League presidents, Shafik was ...

Reported Speech Exercises with Answers for Class 10

Reported Speech Exercises for Class 10 with Answers

Change as directed

Frequently Asked Questions

Is knowing reported speech necessary for Class 10?

What is indirect speech?

Leave a Comment Cancel reply

Register with BYJU'S & Download Free PDFs

25,000+ students realised their study abroad dream with us. Take the first step today

Reported Speech For Class 10: Exciting Exercises with Answers [PDF]

This Blog Includes:

Amanpreet Kaur

8 Universities with higher ROI than IITs and IIMs

Connect With Us

Need help with?

Country Preference

Which English test are you planning to take?

When are you planning to take the exam?

Which Degree do you wish to pursue?

What is your budget to study abroad?

Make your study abroad dream a reality in January 2022 with

Essex Direct Admission Day

CBSE Class 10 English Grammar – Direct And Indirect Speech

Reported Speech Exercises for Class 10 CBSE With Answers

Reported Speech Solved Examples Exercises for Class 10 CBSE

Reported Speech Dialogue Exercises for Class 10 CBSE With Answers

Reported Speech Dialogue Exercises For Class 10 Cbse With Answers PDF

Reported Speech Exercises Solved Example With Answers for Class 10 CBSE

Grammar | Reported Speech

In the chapter "Reported Speech," students learn how to report what someone else has said. This involves changing the tense of the original sentence, as well as making changes to pronouns, time expressions, and other words.

Introduction to CBSE Solutions for Class 10 English Chapter: Reported Speech

Questions and Answers for CBSE Class 10 English Chapter: Reported Speech

Want access to all premium tests, videos & docs?

Augmented Storytelling

Content Pedagogy

Download the Mobile App to watch more videos.

In Partnership with

Quick Links

+91 080 41650688

Reported Speech Exercises for Class 10 CBSE With Answers

Online Education for Reported Speech Exercises for Class 10 CBSE With Answers Pdf

Reported Speech Solved Examples Exercises for Class 10 CBSE

7. Reported speech

Enter your mobile number to sign up in EduMple

Direct and Indirect Speech Exercises for Class 10 with Answers - Download Free PDF With Solutions

Direct and Indirect Speech Exercises for Class 10 with Answers

Indirect or Reported Speech Class 10

Benefits of Learning Reported Speech Class 10

Basic Rules of Direct and Indirect Speech that Students of Class 10 Should Know

Rules of Direct Speech for Class 10

Rules of Reported Speech Class 10

Direct to Indirect Speech Conversion

Important Topics for Class 10 Direct and Indirect Speech

Reported Speech Exercises for Class 10 with Answers

Rules for Reported Speech Class 10

Reported Speech Class 10 Solved Examples Exercises for CBSE Board

Why Should You Download Direct and Indirect Speech Exercises for Class 10 with Answers Free PDF ?

FAQs on Direct and Indirect Speech Class 10 CBSE English

Suggested Videos

Browse more Topics under Transformation Sentences

Reported speech- Play of the tenses:

Reported speech- Handling questions:

Reported speech- Reported requests:

Reported speech- Reported orders:

Reported speech- Time transitions:

Customize your course in 30 seconds

Transformation of Sentences

37 responses to “Active and Passive voice”

Leave a Reply Cancel reply

Download the App

Reported Speech Worksheet for Class 10 CBSE

Related posts:

Leave a Reply Cancel reply

Explore perfectyourenglish.com

Never Miss an Update!

Sites in our network

Recent Posts

Reported Speech Worksheet For Class 10 CBSE

More direct and indirect speech worksheets

Related worksheets