computer based speech and language therapy

Computer Programs for Speech Therapy

Image source: isafesoft.com

Speech therapy apps are great for fitting in a quick lesson on the way to soccer practice. However, you can also turn your PC into an electronic speech therapy tutor for your child. Some computer programs for speech therapy are customized for a specific speech disorder, while others offer a comprehensive range of tools. Consult your child’s speech-language pathologist (SLP) and ask for recommendations of software programs that would best suit your child’s needs.

Video Voice Speech Training System

Video Voice Speech Training System is appropriate for children of all ages, as well as adults. Video Voice is easy to navigate and offers complementary software updates. Your child can use the various games and displays in this software to work on his articulation, sound production, rate of speech, and more. Video Voice is intended to be used by those with a wide range of speech disorders and related conditions, including apraxia, hearing impairment, oral motor articulation deficits, autism, head injuries, and more.

Image source: videovoice.com

TalkTime with Tucker

TalkTime with Tucker  is a voice-activated speech therapy software program that is appropriate for children from pre-kindergarten through the fourth grade. This program’s primary function is to encourage vocalization in reluctant talkers. Children can make Tucker, an animated character, move and talk by speaking into a microphone. The program keeps children engaged by encouraging them to guide Tucker through various adventures.

Tiger’s Tale

Tiger’s Tale is appropriate for children in preschool and elementary school. It elicits oral communication by encouraging children to speak for a tiger that has lost his voice. This type of software can benefit children with articulation, fluency, voice, and language disorders. As the tiger goes on a search for his voice, children can record their voices. When the tiger’s search is complete, children can play back the “movie” to hear their own voices.

Image source: bridgesontario.wordpress.com

Clicker 6  is a software program that can benefit children who struggle with an expressive language disorder . Your child can use the sentence builder grids to learn proper sentence structure. He can also use the “forced order” grids, in which he is given a sample of words to put in the correct order. Clicker 6 also offers tools for your child to record his voice. He may listen to a model sentence and then attempt to imitate it. This software program also offers sound matching games and additional activities to strengthen a child’s expressive language abilities. Children who have little to no speech may also use the program as an augmentative and alternative communication (AAC) device.

Hello my name is evelyn i have a 6 year old son he does talk much just simple word can u send me info on speech i want to put him n anything i can to help him please he has austim.

Comments are closed.

Advertisement

Virtual Speech-Language Therapy for Individuals with Communication Disorders: Current Evidence, Limitations, and Benefits

• Communication Disorders (J Sigafoos, Section Editor)
• Published: 25 May 2019
• Volume 6 , pages 119–125, ( 2019 )

Cite this article

• Sue Ann S. Lee 1  

1238 Accesses

7 Citations

3 Altmetric

Explore all metrics

Purpose of Review

To review currently available research on virtual therapy for individuals with speech-language disorders. Virtual speech-language therapy refers to speech-language therapy delivered via computer-simulation technology to improve speech and language abilities for children and adults with communication disorders. In addition, this paper addressed limitations of previous studies and benefits of virtual therapy.

Recent Findings

Recent research reports suggest that virtual therapy is a viable option for treating children and adults with speech-language disorders. However, most current research studies were conducted without a rigorous experimental design. Also, the quality of technology is indeterminate and application to real clinical practice is currently lacking. More studies should be conducted with a rigorous experimental design as well as advanced technology in the future.

This paper has reviewed current evidence related to the effectiveness of virtual speech-language intervention for children and adults with speech-language impairment. This paper discussed limitations of the current research and benefits of virtual therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Using Socially Assistive Robots in Speech-Language Therapy for Children with Language Impairments

Speech Therapy: Being Understood Clearly

A Systematic Review of Research Comparing Mobile Technology Speech-Generating Devices to Other AAC Modes with Individuals with Autism Spectrum Disorder

Explore related subjects.

• Artificial Intelligence

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

Merrian-Webster. Retrieved on January 20, 2019 from https://www.merriam-webster.com/dictionary/virtual

•• Chen YP, Johnson C, Lalbakhsh P, Caelli T, Deng G, Tay D, et al. Systematic review of virtual speech therapists for speech disorders. Comput Speech Lang. 2016;37:98–128. https://doi.org/10.1016/j.csl.2015.08.005 This systematic review includes quantitative and qualitative research of virtual therapy.

Article   Google Scholar  

American Speech-Language-Hearing Association. Retrieved on January 20, 2019 from https://www.asha.org/practice-portal/professional-issues/telepractice/

Alcañiz M, Botella C, Baños R, Perpiñá C, Rey B, Lozano J, et al. The construction of an online virtual reality treatment system. Annual Rev Cybertherapy Teleme. 2003;1:19–27 Retrieved from http://www.arctt.info/volume-1%2D%2Dsummer-2003 . Accessed 20 Jan 2019.

Bird ML, Cannell J, Jovic E, Rathjen A, Lane K, Tyson A, et al. Randomized controlled trial investigating the efficacy of virtual reality in inpatient stroke rehabilitation. Arch Phys Med Rehabil. 2017;98:e27. https://doi.org/10.1016/j.apmr.2017.08.084 .

Albiol-Perez S, Gil-Gomez JA, Munoz-Tomas MT, Gil-Gomez H, Vial-Escolano R, Lozano-Quilis JA. The effect of balance training on postural control in patients with Parkinson’s disease using a virtual rehabilitation system. Methods Inf Med. 2017;56:138–44. https://doi.org/10.3414/me16-02-0004 .

Article   PubMed   Google Scholar  

Casuso-Holgado MJ, Martin-Valero R, Carazo AF, Medrano-Sanchez EM, Cortes-Vega MD, Montero-Bancalero FJ. Effectiveness of virtual reality training for balance and gait rehabilitation in people with multiple sclerosis: a systematic review and meta analysis. Clinical Rehab. 2018;32:1120–234. https://doi.org/10.1177/0269215518768084 .

Mishkin MC, Norr AM, Katz AC, Reger GM. Review of virtual reality treatment in psychiatry: evidence versus current diffusion and use. Curr Psychiatry Rep. 2017;19:80. https://doi.org/10.1007/s11920-017-0836-0 .

Orlosky J, Itoh Y, Ranchet M, Kiyokawa K, Morgan J, Devos H. Emulation of physician tasks in eye-tracked virtual reality for remote diagnosis of neurodegenerative disease. IEEE Trans Vis Comput Graph. 2017;23:1302–11. https://doi.org/10.1109/tvcg.2017.2657018 .

Areces D, Rodríguez C, García T, Cueli M, González-Castro P. Efficacy of a continuous performance test based on virtual reality in the diagnosis of ADHD and its clinical presentations. J Atten Disord. 2016;1:1–11. https://doi.org/10.1177/1087054716629711 .

Phé V, Cattarino S, Parra J, Bitker MO, Ambrogi V, Vaessen C, et al. Outcomes of a virtual-reality simulator-training programme on basic surgical skills in robot-assisted laparoscopic surgery. Int J Med Robot Comput Assist Surg. 2016;(13):e1740. https://doi.org/10.1002/rcs.1740 .

Pulijala Y, Ma M, Pears M, Peebles D, Ayoub A. Effectiveness of immersive virtual reality in surgical training—a randomized control trial. J Oral Maxillofac Surg. 2018;76:1065–72. https://doi.org/10.1016/j.joms.2017.10.002 .

• Cherney LR, Halper AS, Holland AL, Cole R. Computerized script training for aphasia: preliminary results. Am J Speech Lang Path. 2008;17:19–34. https://doi.org/10.1044/1058-0360(2008/003) This paper is one of the first studies to utilize virtual therapy in individuals with aphasia.

Thompson CK, Choy JJ, Holland A, Sentactics CR. Computer automated treatment of underlying forms. Aphasiology. 2010;24:1242–66. https://doi.org/10.1080/02687030903474255 .

Article   PubMed   PubMed Central   Google Scholar  

Lee J, Kaye RC, Cherney LR. Conversational script performance in adults with nonfluenet aphasia: treatment intensity and aphasia severity. Aphasiology. 2009;23:885–97. https://doi.org/10.1080/02687030802669534 .

•• Vuuren SV, Cherney LR. Virtual therapist for speech and language therapy. Intell Virtual Agents. 2014;1:438–48. https://doi.org/10.1007/978-3-319-09767-1_55 This book chapter provides a good summary of virtual therapy for individuals with aphasia.

Cole R, Halpern A, Ramig L, Vuuren SV, Ngampatipatpong N, Yan J. A virtual speech therapist for individuals with Parkinson’s. Dis Educ tech 2017: 47: 51–55. Retrieved from https://www.researchgate.net/publication/253158009 . Accessed 20 Jan 2019.

•• Kalinyak-Fliszar M, Martin N, Keshner E, Rudnicky A, Shi J, Teodoro G. Using virtual technology to promote functional communication in aphasia: preliminary evidence from interactive dialogues with human and virtual clinicians. Am J Speech Lang Path. 2015;24:974–89. https://doi.org/10.1044/2015_ajslp-14-0160 This study compared virtual clinicians and human clinicians .

•• Abad A, Pompili A, Costa A, Trancoso I, Fonseca J, Leal G, et al. Automatic word naming recognition for an online aphasia treatment system. Comput Speech Lang. 2013;27:1235–48. https://doi.org/10.1016/j.csl.2012.10.003 This study utilized both virtual therapy and telepractice .

•• Marshall J, Booth T, Devane N, Galliers J, Greenwood H, Hilari K, et al. Evaluating the benefits of aphasia intervention delivered in virtual reality: results of a quasi-randomized study. PLoS One. 2016;12:1–18. https://doi.org/10.1371/journal.pone.0160381 This study utilized both virtual therapy and telepractice .

Amaya A, Woolf C, Devane N, Galliers J, Talbot R, Wilson S, et al. Receiving aphasia intervention in a virtual environment: the participants’ perspective. Aphasiology. 2018;32:538–58. https://doi.org/10.1080/02687038.2018.1431831 .

Brundage SB, Graap K, Gibbons KF, Ferrer M, Brooks J. Frequency of stuttering during challenging and supportive virtual reality job interviews. J Fluency Dis. 2006;31:325–39. https://doi.org/10.1016/j.jfludis.2006.08.003 .

•• Brundage SB, Hancock AB. Real enough: using virtual public speaking environments to evoke feelings and behaviors targeted in stuttering assessment and treatment. Am J Speech Lang Path. 2015;24:139–49. https://doi.org/10.1044/2014_ajslp-14-0087 This study tested the effect of virtual therapy with individuals who stutter .

Brundage SB, Brinton JM, Hancock AB. Utility of virtual reality environments to examine physiological reactivity and subjective distress in adults who stutterer. J Fluency Dis. 2016;50:85–95. https://doi.org/10.1016/j.jfludis.2016.10.001 .

• Massaro DW, Light J. Using visible speech to train perception and production of speech for individuals with hearing loss. J Speech Lang Hear Res. 2004;47:304–20. https://doi.org/10.1044/1092-4388(2004/025) This study examined the effect of virtual therapy with individuals with hearing loss.

Fagel S, Madany K. A 3-D virtual head as a tool for speech therapy for children. Interspeech. 2008 retrieved from https://www.researchgate.net/publication/221489242

Saz O, Yin SC, Lleid E, Rose R, Vaquero C, Rodríguez WR. Tools and technologies for computer-aided speech and language therapy. Speech Comm. 2009;51:948–67. https://doi.org/10.1016/j.specom.2009.04.006 .

Schipor OA, Pentiuc SG, Schipor MD. Improving computer based speech therapy using a fuzzy expert system. Comput Inform. 2010;29:303–18 Retrieved from https://www.researchgate.net/publication/220106359 . Accessed 20 Jan 2019.

Chien CH, Che, CH, Jeng TS. An interactive augmented reality system for learning anatomy structure. Proceedings of the international multiconference of engineers and computer scientists (IMECS 2010) 2018. Retrieved from https://pdfs.semanticscholar.org/7dba/e2e9a5f2f5ec255b4f711bf20fe49c89b712.pdf?_ga=2.59156203.752088638.1557749143-1785422099.1554349218 . Accessed 20 Jan 2019.

• Hayden, CM. Augmented reality for speech and language intervention in autism spectrum disorders. 2017. Unpublished master’s thesis. University of Texas at Austin. Retrieved from https://repositories.lib.utexas.edu/handle/2152/47361 . This thesis provides a summary of augmented reality for children with autism spectrum disorders.

• Mesa-Gresa P, Gil-Gómez H, Lozano-Quilis JA, Gil-Gómez JA. Effectiveness of virtual reality for children and adolescents with autism spectrum disorders: an evidence-based systematic review. Sensor. 2018;18:2486. https://doi.org/10.3390/s18082486 This systematic review provides a summary of virtual reality for individuals with autism spectrum disorders.

Bernardini S, Porayska-Pomsta K, Sampath H. Designing an intelligent virtual agent for social communication in autism. Association for the advancement of artificial intelligence. 2013.

Google Scholar  

•• Parsons S. Learning to work together: designing a multi-user virtual reality game for social collaboration and perspective-taking for children with autism. Inter J Child Comp Interaction. 2015;6:28–38. https://doi.org/10.1016/j.ijcci.2015.12.002 This paper utilized game for children with autism spectrum disorders .

Bai Z, Blackwell AF, Coulouris G. Using augmented reality to elicit pretend play for children with autism. IEEE Trans Vis Comput Graph. 2015;21:598–610. https://doi.org/10.1109/tvcg.2014.2385092 .

•• Chen CH, Lee IJ, Lin LY. Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. In: Res in Dev Disabilities, vol. 36; 2015. p. 396–403. https://doi.org/10.1016/j.ridd.2014.10.015 . This study utilized realistic facial expressions.

Chapter   Google Scholar  

•• Chen CH, Lee IJ, Lin LY. Augmented reality-based video-modeling storybook of nonverbal facial cues for children with autism spectrum disorder to improve their perceptions and judgments of facial expressions and emotions. Comput Hum Behav. 2016;55:477–9. https://doi.org/10.1016/j.chb.2015.09.033 This is a well designed single subject experimental study .

da Silva CA, Fernandes AR, Grohmann AP. STAR: Speech therapy with augmented reality for children with autism spectrum disorders (pp. 379–396). Springer International Publishing. 2015. Retrieved from https://doi.org/10.1007/978-3-319-22348-3_21

Taryadi B, Kurniawan I. The improvement of autism spectrum disorders on children communication ability with PECS method multimedia augmented reality-based. J Phys Conf Ser. 2018;947:012009. https://doi.org/10.1088/1742-6596/947/1/012009 .

Blade RA, Padgett ML: Virtual Environments: History and profession. In: Stanney KM, Hale KS, editors. Handbook of virtual environments: design, implement, and application. Mahwah: Lawrence Erlbaum associate; 2002.

Wise B, Cole R, Vuuren SV, Schwartz S, Snyder L, Ngampatipatpong N, Tuantranont J, Pellom B. Learning to read with a virtual tutor. Retrieved from https://www.researchgate.net/publication/252175880 . Accessed 20 Jan 2019.

Burdea G. Virtual rehabilitation: benefits and challenges. Methods Inf Med. 2003;42:519–23. https://doi.org/10.1055/s-0038-1634378 .

Article   CAS   PubMed   Google Scholar  

Holden M. Virtual environments for motor rehabilitation. CyberPsychology Behavior. 2005;8:187–219. https://doi.org/10.1162/105474605775196580 .

Huet M, Jacobs D, Camachon C, Goulon C, Montagne G. Self-controlled concurrent feedback facilitates the learning of the final approach phase in a fixed-based flight simulator. Hum Factors. 2009;51:858–71. https://doi.org/10.1177/0018720809357343 .

Rosenblatt RA, Hart LG. Physicians and rural America. West J Med. 2000;173:348–51. https://doi.org/10.1136/ewjm.173.5.348 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Download references

Author information

Authors and affiliations.

Texas Tech University Health Sciences Center, 3601 4th Street, MS 6031, Lubbock, TX, 79430, USA

Sue Ann S. Lee

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sue Ann S. Lee .

Ethics declarations

Conflict of interest.

The author declares no conflicts of interest relevant to this manuscript.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s note.

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

This article is part of the Topical Collection on Communication Disorders

Rights and permissions

Reprints and permissions

About this article

Lee, S.A.S. Virtual Speech-Language Therapy for Individuals with Communication Disorders: Current Evidence, Limitations, and Benefits. Curr Dev Disord Rep 6 , 119–125 (2019). https://doi.org/10.1007/s40474-019-00169-7

Download citation

Published : 25 May 2019

Issue Date : 15 September 2019

DOI : https://doi.org/10.1007/s40474-019-00169-7

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

• Virtual therapy
• Speech-language intervention
• Virtual therapist
• Virtual reality
• Augmented reality
• Find a journal
• Publish with us
• Track your research

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A systematic review of online speech therapy systems for intervention in childhood speech communication disorders.

1. Introduction

2. background and related works, 2.1. communication disorders, 2.2. telepractice and ost, 2.3. speech recognition, 2.4. machine learning, 2.5. related work, 3. research methodology, 3.1. research questions.

• RQ1.1: For which goals/context have OST Systems been developed?
• RQ1.2: What are the features of the OST systems?
• RQ1.3: For which target groups have OST systems been used?
• RQ1.4: What are the adopted architecture designs of the OST systems?
• RQ1.5: What are the adopted ML approaches in these OST systems?
• RQ1.6: What are the properties of the software used for these systems?
• RQ2.1: Which evaluation approaches have been used to assess the efficacy of the OST systems?
• RQ2.2: Which performance metrics have been used to gauge the efficacy of the OST systems?

3.2. Search Strategy

3.2.1. search scope, 3.2.2. search method, 3.3. study selection criteria, 3.4. study quality assessment, 3.5. data extraction and monitoring, 3.6. data synthesis and reporting, 4.1. for which goals/context have ost systems been developed, 4.2. what are the features of the ost systems.

• Audio feedback is audio output from a system that informs the user whether he or she is performing well.
• Emotion screening: The system considers the client’s emotions throughout the session, for example, by measuring the facial expressions with a face tracker or the system asking the child how he or she feels or to rate the child’s feelings.
• Error detection: Identification of errors made through speech analysis algorithms by analyzing produced vowels and consonants individually (Parnandi et al., 2013 [ 39 ]).
• Peer-to-peer feedback is a feature that enables multiple clients to participate with each other in an exercise. Peers can provide feedback to each other’s performance in terms of understandability, the volume of sound and so on, depending on the exercise’s context and scope.
• Speech recognition, also known as speech-to-text or automatic speech recognition, is a feature that enables a program to convert human speech into a written format.
• Recommendation strategy: A feature that provides suggestions for helpful follow-up exercises and activities that can be undertaken by the SLP based on the correctness of pronunciation (Franciscatto et al., 2021 [ 1 ]).
• Reporting: Providing statistical reports about the child’s progress and the level of performance during the session.
• Text-to-speech: A feature that can read digital text on a digital device aloud.
• Textual feedback is textual output from a system that shows the user whether he performs well. For example, when the word is pronounced correctly, the text “Correct Answer” appears, whereas if the word is mispronounced, the text “Incorrect Answer” appears, possibly with an explanation of why it is incorrect.
• User data management: Everything that has to do with keeping track of the personal data of clients, such as account names and age.
• User voice recorder: A feature that provides the option to record the spoken text by the clients. The recorded voice can be played back by, for example, the client, the SLP or other actors such as a teacher or parent.
• A virtual 3D model aids in viewing the correct positioning of the lips, language and teeth for each sound (Danubianu, 2016).
• Visual feedback is visual output from a system, such as a video game, that shows the user if he or she is performing well or not. For example, a character only proceeds to the next level when the client has pronounced the word correctly. Visual feedback is the character’s movement from level A to level B, as illustrated in Figure 4 .
• Voice commands are spoken words by the child that let the system act. For example, when a child says jump, the character in a game jumps.

4.3. For Which Target Groups Have OST Systems Been Used?

4.4. what are the adopted architecture designs of the ost systems, 4.4.1. client–server system, 4.4.2. repository pattern, 4.4.3. layered approach, 4.4.4. standalone system, 4.4.5. pipe-and-filter architecture, 4.5. what are the adopted machine learning approaches in these ost systems, 4.6. what are the properties of the software used for these systems, 4.7. which evaluation approaches have been used to assess the efficacy of the ost systems, 4.7.1. case study, 4.7.2. experimental, 4.7.3. observational, 4.7.4. simulation-based, 4.7.5. not evaluated, 4.8. which evaluation metrics have been used to gauge the efficacy of the ost systems.

• Accuracy describes the percentage of correctly predicted values.
• Recall is the proportion of all true positives predicted by the model divided by the total number of predicted values [ 15 ]. R e c a l l = T P T P + F N . TP = true positives. FN = false negatives.
• F1-score is a summary of both recall and precision (Russell and Norvig, 2010). F 1 − S c o r e = 2 ∗ P r e c i s i o n * R e c a l l P r e c i s i o n + R e c a l l .
• Precision calculates the proportion of correctly identified positives (Russell and Norvig, 2010). P r e c i s i o n = T P T P + F P . TP = true positives. FP = false positives.
• Pearson’s r is a statistical method that calculates the correlation between two variables.
• Root-mean-square deviation (RMSE) calculates the difference between the predicted values and the observed values.
• R M S E = ∑ i = 1 N ( P r e d i c t e d i − A c t u a l i ) 2 N .
• Kappa is a method that compares the observed and expected values.
• Error refers to the average error of the system regarding its measures.
• Usability refers to the effectiveness, efficiency and satisfaction together [ 27 ].
• Satisfaction refers to how pleasant or comfortable the use of the application is [ 43 , 47 ].
• Efficiency refers to the resources spent to achieve effectiveness, such as time to complete the task, the mistakes made and difficulties encountered [ 27 ].
• Effectiveness refers to the number of users that can complete the tasks without quitting [ 27 ].
• Reliability refers to the level at which the application responds correctly and consistently regarding its purpose [ 29 ].
• Sensitivity is the level at which the tool can discriminate the proper pronunciations from the wrong ones [ 32 ].
• Coherence specifies whether the exercises selected by the system are appropriate for the child, according to their abilities and disabilities [ 41 ].
• Completeness determines if the plans recommended by the expert system are complete and takes into account the areas in which the child should be trained to develop specific skills (according to the child’s profile) [ 41 ].
• Relevance determines if each exercise’s specificity is appropriate for the child [ 41 ].
• Ease of learning memorization looks at how easy it is for the user to perform simple tasks using the interface for the first time [ 47 ].

5. Discussion and Limitations of the Review

6. conclusions, author contributions, data availability statement, conflicts of interest, appendix a. quality assessment form.

Appendix B. Data Extraction Form

Appendix C. Sample Size Characteristics of Experimental Studies

• Franciscatto, M.H.; Del Fabro, M.D.; Damasceno Lima, J.C.; Trois, C.; Moro, A.; Maran, V.; Keske-Soares, M. Towards a speech therapy support system based on phonological processes early detection. Comput. Speech Lang. 2021 , 65 , 101130. [ Google Scholar ] [ CrossRef ]
• Danubianu, M.; Pentiuc, S.G.; Andrei Schipor, O.; Nestor, M.; Ungureanu, I.; Maria Schipor, D. TERAPERS—Intelligent Solution for Personalized Therapy of Speech Disorders. Int. J. Adv. Life Sci. 2009 , 1 , 26–35. [ Google Scholar ]
• Robles-Bykbaev, V.; López-Nores, M.; Pazos-Arias, J.J.; Arévalo-Lucero, D. SPELTA: An expert system to generate therapy plans for speech and language disorders. Expert Syst. Appl. 2015 , 42 , 7641–7651. [ Google Scholar ] [ CrossRef ]
• American Speech-Language-Hearing Association. Speech Sound Disorders: Articulation and Phonology: Overview ; American Speech-Language-Hearing Association: Rockville, MD, USA, 2017. [ Google Scholar ]
• Toki, E.I.; Pange, J.; Mikropoulos, T.A. An online expert system for diagnostic assessment procedures on young children’s oral speech and language. In Proceedings of the Procedia Computer Science, San Francisco, CA, USA, 28–30 October 2012; Elsevier B.V.: Amsterdam, The Netherlands, 2012; Volume 14, pp. 428–437. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ben-Aharon, A. A practical guide to establishing an online speech therapy private practice. Perspect. Asha Spec. Interest Groups 2019 , 4 , 712–718. [ Google Scholar ] [ CrossRef ]
• Furlong, L.; Erickson, S.; Morris, M.E. Computer-based speech therapy for childhood speech sound disorders. J. Commun. Disord. 2017 , 68 , 50–69. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mcleod, S.; Baker, E. Speech-language pathologists’ practices regarding assessment, analysis, target selection, intervention, and service delivery for children with speech sound disorders. Clin. Linguist. Phon. 2014 , 28 , 508–531. [ Google Scholar ] [ CrossRef ]
• Sasikumar, D.; Verma, S.; Sornalakshmi, K. A Game Application to assist Speech Language Pathologists in the Assessment of Children with Speech Disorders. Int. J. Adv. Trends Comput. Sci. Eng. 2020 , 9 , 6881–6887. [ Google Scholar ] [ CrossRef ]
• Rodríguez, W.R.; Saz, O.; Lleida, E. A prelingual tool for the education of altered voices. Speech Commun. 2012 , 54 , 583–600. [ Google Scholar ] [ CrossRef ]
• Chen, Y.P.P.; Johnson, C.; Lalbakhsh, P.; Caelli, T.; Deng, G.; Tay, D.; Erickson, S.; Broadbridge, P.; El Refaie, A.; Doube, W.; et al. Systematic review of virtual speech therapists for speech disorders. Comput. Speech Lang. 2016 , 37 , 98–128. [ Google Scholar ] [ CrossRef ]
• Yu, D.; Deng, L. Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology) ; Springer: Berlin/Heidelberg, Germany, 2014. [ Google Scholar ]
• Ghai, W.; Singh, N. Literature Review on Automatic Speech Recognition. Int. J. Comput. Appl. 2012 , 41 , 975–8887. [ Google Scholar ] [ CrossRef ]
• Padmanabhan, J.; Premkumar, M.J.J. Machine learning in automatic speech recognition: A survey. IETE Tech. Rev. (Inst. Electron. Telecommun. Eng. India) 2015 , 32 , 240–251. [ Google Scholar ] [ CrossRef ]
• Russell, S.; Norvig, P. Artificial Intelligence A Modern Approach , 3rd ed.; Prentice Hall: Hoboken, NJ, USA, 2010. [ Google Scholar ]
• Dede, G.; Sazli, M.H. Speech recognition with artificial neural networks. Digit. Signal Process. Rev. J. 2010 , 20 , 763–768. [ Google Scholar ] [ CrossRef ]
• Lee, S.A.S. This review of virtual speech therapists for speech disorders suffers from limited data and methodological issues. Evid. Based Commun. Assess. Interv. 2018 , 12 , 18–23. [ Google Scholar ] [ CrossRef ]
• Jesus, L.M.; Santos, J.; Martinez, J. The Table to Tablet (T2T) speech and language therapy software development roadmap. JMIR Res. Protoc. 2019 , 8 , e11596. [ Google Scholar ] [ CrossRef ]
• Kitchenham, B.; Charters, S. Guidelines for performing systematic literature reviews in software engineering. In Technical Report, Ver. 2.3 EBSE Technical Report ; EBSE: Goyang, Republic of Korea, 2007. [ Google Scholar ]
• Belen, G.M.A.; Cabonita, K.B.P.; dela Pena, V.A.; Dimakuta, H.A.; Hoyle, F.G.; Laviste, R.P. Tingog: Reading and Speech Application for Children with Repaired Cleft Palate. In Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 29 November–2 December 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [ Google Scholar ] [ CrossRef ]
• Cagatay, M.; Ege, P.; Tokdemir, G.; Cagiltay, N.E. A serious game for speech disorder children therapy. In Proceedings of the 2012 7th International Symposium on Health Informatics and Bioinformatics, Nevsehir, Turkey, 19–22 April 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 18–23. [ Google Scholar ] [ CrossRef ]
• Cavaco, S.; Guimarães, I.; Ascensão, M.; Abad, A.; Anjos, I.; Oliveira, F.; Martins, S.; Marques, N.; Eskenazi, M.; Magalhães, J.; et al. The BioVisualSpeech Corpus of Words with Sibilants for Speech Therapy Games Development. Information 2020 , 11 , 470. [ Google Scholar ] [ CrossRef ]
• Danubianu, M.; Pentiuc, S.G.; Schipor, O.A.; Tobolcea, I. Advanced Information Technology—Support of Improved Personalized Therapy of Speech Disorders. Int. J. Comput. Commun. Control. 2010 , 5 , 684. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Das, M.; Saha, A. An automated speech-language therapy tool with interactive virtual agent and peer-to-peer feedback. In Proceedings of the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 28–30 September 2017; IEEE: Piscataway, NJ, USA, 2017; Volume 2018, pp. 510–515. [ Google Scholar ] [ CrossRef ]
• Firdous, S.; Wahid, M.; Ud, A.; Bakht, K.; Yousuf, M.; Batool, R.; Noreen, M. Android based Receptive Language Tracking Tool for Toddlers. Int. J. Adv. Comput. Sci. Appl. 2019 , 10 , 589–595. [ Google Scholar ] [ CrossRef ]
• Geytenbeek, J.J.; Heim, M.M.; Vermeulen, R.J.; Oostrom, K.J. Assessing comprehension of spoken language in nonspeaking children with cerebral palsy: Application of a newly developed computer-based instrument. AAC Augment. Altern. Commun. 2010 , 26 , 97–107. [ Google Scholar ] [ CrossRef ]
• Gonçalves, C.; Rocha, T.; Reis, A.; Barroso, J. AppVox: An Application to Assist People with Speech Impairments in Their Speech Therapy Sessions. In Proceedings of the 2017 World Conference on Information Systems and Technologies (WorldCIST’17), Madeira, Portugal, 11–13 April 2017; pp. 581–591. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hair, A.; Monroe, P.; Ahmed, B.; Ballard, K.J.; Gutierrez-Osuna, R. Apraxia world: A Speech Therapy Game for Children with Speech Sound Disorders. In Proceedings of the 17th ACM Conference on Interaction Design and Children, Stanford, CA, USA, 27–30 June 2017; ACM: New York, NY, USA, 2017; pp. 119–131. [ Google Scholar ] [ CrossRef ]
• Jamis, M.N.; Yabut, E.R.; Manuel, R.E.; Catacutan-Bangit, A.E. Speak App: A Development of Mobile Application Guide for Filipino People with Motor Speech Disorder. In Proceedings of the IEEE Region 10 Annual International Conference, Proceedings/TENCON, Kochi, India, 17–20 October 2019; pp. 717–722. [ Google Scholar ] [ CrossRef ]
• Kocsor, A.; Paczolay, D. Speech technologies in a computer-aided speech therapy system. In Proceedings of the 10th International Conference, ICCHP 2006, Linz, Austria, 11–13 July 2006; 2006; pp. 615–622. [ Google Scholar ]
• Kraleva, R.S. ChilDiBu—A Mobile Application for Bulgarian Children with Special Educational Needs. Int. J. Adv. Sci. Eng. Inf. Technol. 2017; 7. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kröger, B.J.; Birkholz, P.; Hoffmann, R.; Meng, H. Audiovisual Tools for Phonetic and Articulatory Visualization in Computer-Aided Pronunciation Training. In Development of Multimodal Interfaces: Active Listening and Synchrony ; Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2010; Volume 5967 LNCS, pp. 337–345. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Madeira, R.N.; Macedo, P.; Pita, P.; Bonança, Í.; Germano, H. Building on Mobile towards Better Stuttering Awareness to Improve Speech Therapy. In Proceedings of the International Conference on Advances in Mobile Computing & Multimedia-MoMM ’13, Vienna, Austria, 2–4 December 2013; ACM Press: New York, NY, USA, 2013; pp. 551–554. [ Google Scholar ] [ CrossRef ]
• Martínez-Santiago, F.; Montejo-Ráez, A.; García-Cumbreras, M.Á. Pictogram Tablet: A Speech Generating Device Focused on Language Learning. Interact. Comput. 2018 , 30 , 116–132. [ Google Scholar ] [ CrossRef ]
• Nasiri, N.; Shirmohammadi, S. Measuring performance of children with speech and language disorders using a serious game. In Proceedings of the 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, 7–10 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 15–20. [ Google Scholar ] [ CrossRef ]
• Ochoa-Guaraca, M.; Carpio-Moreta, M.; Serpa-Andrade, L.; Robles-Bykbaev, V.; Lopez-Nores, M.; Duque, J.G. A robotic assistant to support the development of communication skills of children with disabilities. In Proceedings of the 2016 IEEE 11th Colombian Computing Conference, CCC 2016—Conference Proceedings, Popayán, Colombia, 27–30 September 2016. [ Google Scholar ] [ CrossRef ]
• Origlia, A.; Altieri, F.; Buscato, G.; Morotti, A.; Zmarich, C.; Rodá, A.; Cosi, P. Evaluating a multi-avatar game for speech therapy applications. In Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good-Goodtechs ’18, Bologna, Italy, 28–30 November 2018; ACM Press: New York, New York, USA, 2018; pp. 190–195. [ Google Scholar ] [ CrossRef ]
• Parmanto, B.; Saptono, A.; Murthi, R.; Safos, C.; Lathan, C.E. Secure telemonitoring system for delivering telerehabilitation therapy to enhance children’s communication function to home. Telemed. e-Health 2008 , 14 , 905–911. [ Google Scholar ] [ CrossRef ]
• Parnandi, A.; Karappa, V.; Son, Y.; Shahin, M.; McKechnie, J.; Ballard, K.; Ahmed, B.; Gutierrez-Osuna, R. Architecture of an automated therapy tool for childhood apraxia of speech. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility, Bellevue, WA, USA, 21–23 October 2013; ACM: New York, NY, USA, 2013; pp. 1–8. [ Google Scholar ] [ CrossRef ]
• Pentiuc, S.; Tobolcea, I.; Schipor, O.A.; Danubianu, M.; Schipor, M. Translation of the Speech Therapy Programs in the Logomon Assisted Therapy System. Adv. Electr. Comput. Eng. 2010 , 10 , 48–52. [ Google Scholar ] [ CrossRef ]
• Redrovan-Reyes, E.; Chalco-Bermeo, J.; Robles-Bykbaev, V.; Carrera-Hidalgo, P.; Contreras-Alvarado, C.; Leon-Pesantez, A.; Nivelo-Naula, D.; Olivo-Deleg, J. An educational platform based on expert systems, speech recognition, and ludic activities to support the lexical and semantic development in children from 2 to 3 years. In Proceedings of the 2019 IEEE Colombian Conference on Communications and Computing (COLCOM), Barranquilla, Colombia, 5–7 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–6. [ Google Scholar ] [ CrossRef ]
• Robles-Bykbaev, V.; Quisi-Peralta, D.; Lopez-Nores, M.; Gil-Solla, A.; Garcia-Duque, J. SPELTA-Miner: An expert system based on data mining and multilabel classification to design therapy plans for communication disorders. In Proceedings of the 2016 International Conference on Control, Decision and Information Technologies (CoDIT), Saint Julian’s, Malta, 6–8 April 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 280–285. [ Google Scholar ] [ CrossRef ]
• Rocha, T.; Gonçalves, C.; Fernandes, H.; Reis, A.; Barroso, J. The AppVox mobile application, a tool for speech and language training sessions. Expert Syst. 2019 , 36 , e12373. [ Google Scholar ] [ CrossRef ]
• Schipor, D.M.; Pentiuc, S.; Schipor, O. End-User Recommendations on LOGOMON - a Computer Based Speech Therapy System for Romanian Language. Adv. Electr. Comput. Eng. 2010 , 10 , 57–60. [ Google Scholar ] [ CrossRef ]
• Sebkhi, N.; Desai, D.; Islam, M.; Lu, J.; Wilson, K.; Ghovanloo, M. Multimodal Speech Capture System for Speech Rehabilitation and Learning. IEEE Trans. Bio-Med. Eng. 2017 , 64 , 2639–2649. [ Google Scholar ] [ CrossRef ]
• Shahin, M.; Ahmed, B.; Parnandi, A.; Karappa, V.; McKechnie, J.; Ballard, K.J.; Gutierrez-Osuna, R. Tabby Talks: An automated tool for the assessment of childhood apraxia of speech. Speech Commun. 2015 , 70 , 49–64. [ Google Scholar ] [ CrossRef ]
• da Silva, D.P.; Amate, F.C.; Basile, F.R.M.; Bianchi Filho, C.; Rodrigues, S.C.M.; Bissaco, M.A.S. AACVOX: Mobile application for augmentative alternative communication to help people with speech disorder and motor impairment. Res. Biomed. Eng. 2018 , 34 , 166–175. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Vaquero, C.; Saz, O.; Lleida, E.; Rodriguez, W.R. E-inclusion technologies for the speech handicapped. In Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA, 31 March–4 April 2008; IEEE: Piscataway, NJ, USA, 2008; pp. 4509–4512. [ Google Scholar ] [ CrossRef ]
• Grossinho, A.; Cavaco, S.; Magalhães, J. An interactive toolset for speech therapy. In Proceedings of the 11th Conference on Advances in Computer Entertainment Technology, Funchal, Portugal, 11–14 November 2014; ACM: New York, NY, USA, 2014; pp. 1–4. [ Google Scholar ] [ CrossRef ]
• Lopes, M.; Magalhães, J.; Cavaco, S. A voice-controlled serious game for the sustained vowel exercise. In Proceedings of the 13th International Conference on Advances in Computer Entertainment Technology, Osaka, Japan, 9–12 November 2016; ACM: New York, NY, USA, 2016; pp. 1–6. [ Google Scholar ] [ CrossRef ]
• Velasquez-Angamarca, V.; Mosquera-Cordero, K.; Robles-Bykbaev, V.; Leon-Pesantez, A.; Krupke, D.; Knox, J.; Torres-Segarra, V.; Chicaiza-Juela, P. An Educational Robotic Assistant for Supporting Therapy Sessions of Children with Communication Disorders. In Proceedings of the 2019 7th International Engineering, Sciences and Technology Conference (IESTEC), Panama City, Panama, 9–11 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 586–591. [ Google Scholar ] [ CrossRef ]
• Zaharia, M.H.; Leon, F. Speech Therapy Based on Expert System. Adv. Electr. Comput. Eng. 2009 , 9 , 74–77. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Attwell, G.A.; Bennin, K.E.; Tekinerdogan, B. A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders. Sensors 2022 , 22 , 9713. https://doi.org/10.3390/s22249713

Attwell GA, Bennin KE, Tekinerdogan B. A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders. Sensors . 2022; 22(24):9713. https://doi.org/10.3390/s22249713

Attwell, Geertruida Aline, Kwabena Ebo Bennin, and Bedir Tekinerdogan. 2022. "A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders" Sensors 22, no. 24: 9713. https://doi.org/10.3390/s22249713

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Accessibility options:

Computerised speech and language therapy can help people with aphasia find words following a stroke

doi: 10.3310/signal-000864

This is a plain English summary of an original research article . The views expressed are those of the author(s) and reviewer(s) at the time of publication.

People with aphasia caused by a stroke show improvements in retrieving words when they use self-managed computerised speech and language therapy in addition to usual care from a speech and language therapist. No improvements are seen in patients’ conversational abilities or their quality of life.

Aphasia is a complex language and communication disorder. It can affect people’s abilities to read, listen, speak, and write or type. Symptoms vary: some people may mix up a few words, while others have problems with all communication. Speech and language therapists work with patients and their carers to help them improve their speech and use alternative ways of communicating, but there is a shortage of therapists.

This well-conducted NIHR-funded trial shows that adding computerised speech and language therapy to usual care can have some benefits, and is a relatively low-cost intervention. It also highlights areas for further research.

Why was this study needed?

Aphasia is usually caused by damage to the left side of the brain, most commonly after a stroke. Around 110,000 people in England have a stroke each year. About a third of survivors will have aphasia. Between 30% and 43% of those affected have symptoms in the long term.

Most people make some improvement with speech and language therapy, and some people recover fully. However, speech and language therapy is resource-intensive and difficult to obtain in the NHS. Some small studies have suggested that computerised therapy might be an effective way to provide additional therapy for those who need it. Computer programmes allow patients to complete exercises to help with word-retrieval and other language problems. They can be tailored for individuals and are readily available.

This study aimed to assess the clinical and cost-effectiveness of self-managed computer speech and language therapy used in addition to usual care.

What did this study do?

Big CACTUS was a randomised controlled trial that recruited 278 adults with aphasia from 20 NHS trusts in the UK.

Participants were randomly assigned to one of three groups. The ‘usual care’ group received support from a speech and language therapist. The ‘computerised speech and language therapy’ group had usual care plus six months of using a computer programme daily at home. This was a self-managed set of word-finding exercises, tailored for each individual. There was also an ‘attention control’ group, who received usual care in addition to completing paper-based puzzle book activities (such as Sudoku, or word searches) daily for six months. This last group helped to ensure that any effect could be attributed to the computer intervention rather than just increased attention from a therapist.

This was a robust, albeit relatively small trial, but it was limited to English speakers, as the computer programme was only available in English.

What did it find?

• On average, participants in the group using a computer had improved word finding of 16.2% more than those in the usual care group (95% confidence interval [CI] 12.7 to 19.6), and 14.4% more than those in the attention control group (95% CI 10.8 to 18.1). This was greater than the pre-specified clinically important difference of 10%. This improvement was maintained at 9 and 12 months.
• The computer therapy did not improve functional communication. Nor did it have an impact on participants’ own perceptions of their communication, social participation or quality of life.
• The mean cost per person for the computer therapy was £733. The cost for the equivalent amount of face-to-face time with a speech and language therapist would be approximately £1,400.

What does current guidance say on this issue?

NICE published guidance on stroke rehabilitation in adults in 2013. Its section on communication states that speech and language therapists should provide direct impairment-based therapy for communication impairments such as aphasia. It doesn’t specify what that therapy should be, or how it should be delivered.

The Royal College of Speech and Language Therapists resource manual for commissioning and planning services for aphasia states that computer-based therapy directed by a speech and language therapist is beneficial, cost-effective and acceptable.

What are the implications?

This study shows that self-managed computerised speech and language therapy can be used alongside usual care to improve patients’ ability to retrieve words. Costs come mainly from the time spent by speech and language therapists setting up the software and providing technical support. This could be done by therapy assistants, which would reduce costs.

However, the benefit was limited to word-finding. It did not improve conversation or quality of life. More research is needed to identify ways of helping patients in these areas. In addition, researchers could evaluate other computer programmes. Programmes in languages other than English might also be worth researching further.

Citation and Funding

Palmer R, Dimairo M, Cooper C et al. Self-managed, computerised speech and language therapy for patients with chronic aphasia post-stroke compared with usual care or attention control (Big CACTUS): a multicentre, single-blinded, randomised controlled trial . Lancet Neurol. 2019;18:821-33.

This project was funded by the NIHR Health Technology Assessment Programme (project number 12/21/01) and the Tavistock Trust for Aphasia.

Bibliography

Brady MC, Kelly H, Godwin J et al. Speech and language therapy for aphasia following stroke . Cochrane Database Syst Rev. 2016;(6):CD000425.

NHS website. Aphasia . London: Department of Health and Social Care; updated 2018.

NICE. Stroke rehabilitation in adults. CG162. London: National Institute for Health and Care Excellence; 2013.

Produced by the University of Southampton and Bazian on behalf of NIHR through the NIHR Dissemination Centre

NIHR Evidence is covered by the creative commons, CC-BY licence . Written content and infographics may be freely reproduced provided that suitable acknowledgement is made. Note, this licence excludes comments and images made by third parties, audiovisual content, and linked content on other websites.

Privacy Overview

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Computer-based speech therapy for childhood speech sound disorders

Affiliations.

• 1 School of Allied Health, College of Science, Health and Engineering, La Trobe University, Bundoora 3086, Melbourne, Australia. Electronic address: [email protected].
• 2 School of Allied Health, College of Science, Health and Engineering, La Trobe University, Bundoora 3086, Melbourne, Australia. Electronic address: [email protected].
• 3 School of Allied Health, College of Science, Health and Engineering, La Trobe University, Bundoora 3086, Melbourne, Australia. Electronic address: [email protected].
• PMID: 28651106
• DOI: 10.1016/j.jcomdis.2017.06.007

Background: With the current worldwide workforce shortage of Speech-Language Pathologists, new and innovative ways of delivering therapy to children with speech sound disorders are needed. Computer-based speech therapy may be an effective and viable means of addressing service access issues for children with speech sound disorders.

Aim: To evaluate the efficacy of computer-based speech therapy programs for children with speech sound disorders.

Method: Studies reporting the efficacy of computer-based speech therapy programs were identified via a systematic, computerised database search. Key study characteristics, results, main findings and details of computer-based speech therapy programs were extracted. The methodological quality was evaluated using a structured critical appraisal tool.

Main contribution: 14 studies were identified and a total of 11 computer-based speech therapy programs were evaluated. The results showed that computer-based speech therapy is associated with positive clinical changes for some children with speech sound disorders.

Conclusions: There is a need for collaborative research between computer engineers and clinicians, particularly during the design and development of computer-based speech therapy programs. Evaluation using rigorous experimental designs is required to understand the benefits of computer-based speech therapy.

Learning outcomes: The reader will be able to 1) discuss how computerbased speech therapy has the potential to improve service access for children with speech sound disorders, 2) explain the ways in which computer-based speech therapy programs may enhance traditional tabletop therapy and 3) compare the features of computer-based speech therapy programs designed for different client populations.

Keywords: Computer-assisted therapy; Computer-based speech therapy; Speech sound disorders.

Copyright © 2017 Elsevier Inc. All rights reserved.

PubMed Disclaimer

Similar articles

• A Systematic Review of Online Speech Therapy Systems for Intervention in Childhood Speech Communication Disorders. Attwell GA, Bennin KE, Tekinerdogan B. Attwell GA, et al. Sensors (Basel). 2022 Dec 11;22(24):9713. doi: 10.3390/s22249713. Sensors (Basel). 2022. PMID: 36560082 Free PMC article. Review.
• Implementation fidelity of a computer-assisted intervention for children with speech sound disorders. McCormack J, Baker E, Masso S, Crowe K, McLeod S, Wren Y, Roulstone S. McCormack J, et al. Int J Speech Lang Pathol. 2017 Jun;19(3):265-276. doi: 10.1080/17549507.2017.1293160. Epub 2017 Mar 29. Int J Speech Lang Pathol. 2017. PMID: 28351159 Clinical Trial.
• Service delivery and intervention intensity for phonology-based speech sound disorders. Sugden E, Baker E, Munro N, Williams AL, Trivette CM. Sugden E, et al. Int J Lang Commun Disord. 2018 Jul;53(4):718-734. doi: 10.1111/1460-6984.12399. Epub 2018 Jun 14. Int J Lang Commun Disord. 2018. PMID: 29900638
• Evidence for the treatment of co-occurring stuttering and speech sound disorder: A clinical case series. Unicomb R, Hewat S, Spencer E, Harrison E. Unicomb R, et al. Int J Speech Lang Pathol. 2017 Jun;19(3):251-264. doi: 10.1080/17549507.2017.1293735. Epub 2017 Mar 14. Int J Speech Lang Pathol. 2017. PMID: 28290729
• Involvement of parents in intervention for childhood speech sound disorders: a review of the evidence. Sugden E, Baker E, Munro N, Williams AL. Sugden E, et al. Int J Lang Commun Disord. 2016 Nov;51(6):597-625. doi: 10.1111/1460-6984.12247. Epub 2016 Mar 27. Int J Lang Commun Disord. 2016. PMID: 27017993 Review.
• Effect of Voice and Articulation Parameters of a Home-Based Serious Game for Speech Therapy in Children With Articulation Disorder: Prospective Single-Arm Clinical Trial. Kim SY, Song M, Jo Y, Jung Y, You H, Ko MH, Kim GW. Kim SY, et al. JMIR Serious Games. 2023 Oct 11;11:e49216. doi: 10.2196/49216. JMIR Serious Games. 2023. PMID: 37819707 Free PMC article.
• Efficacy in the use of gamification strategy in phonological therapy. Silva TFD, Ribeiro GCF, Silva CEED, Assis MF, Dezani H, Berti LC. Silva TFD, et al. Codas. 2023 Sep 8;35(6):e20220181. doi: 10.1590/2317-1782/20232022181pt. eCollection 2023. Codas. 2023. PMID: 37703113 Free PMC article.
• The Table to Tablet (T2T) Speech and Language Therapy Software Development Roadmap. Jesus LM, Santos J, Martinez J. Jesus LM, et al. JMIR Res Protoc. 2019 Jan 30;8(1):e11596. doi: 10.2196/11596. JMIR Res Protoc. 2019. PMID: 30698534 Free PMC article.

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources.

• Elsevier Science

Other Literature Sources

• scite Smart Citations
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Reference architecture design for computer-based speech therapy systems

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, index terms.

Applied computing

Computer systems organization

Architectures

Computing methodologies

Human-centered computing

Human computer interaction (HCI)

Software and its engineering

Software organization and properties

Software system structures

Software architectures

Recommendations

An e-contracting reference architecture.

Business-to-business e-contracting aims at automating the contracting process between companies. It improves the efficiency of the contracting process and enables the introduction of new business models that can be supported by companies. For the ...

Reference architecture for digital twin-based predictive maintenance systems

Digital Twin-based predictive maintenance systems are frequently integrated into complex systems. The success of the integration depends on the design of the system. A Reference Architecture can be used as a blueprint to design ...

• Architecture design approach for designing digital-twin based predictive maintenance systems.
• Feature-driven domain analysis with a detailed feature model on digital-twin based predictive maintenance systems.
• Architecture modeling ...

Software architecture and reference architecture of software-intensive systems and systems-of-systems: contributions to the state of the art

Complex software-intensive systems are more and more required as a solution for diverse critical application domains; at the same time, software architecture and also reference architecture have attracted attention as means to more adequately produce ...

Information

Published in.

Academic Press Ltd.

United Kingdom

Publication History

Author tags.

• Online speech therapy system
• Reference architecture
• Architecture design
• Communication disorder
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

Speech Language Pathologist - 1-5 Days Available

Kaleidoscope education solutions | washington township, nj.

Our client is seeking School-Based Speech-Language Pathologist (SLP) - GRENLOCH, NJ. Serves as a member of the school's multidisciplinary team, IEP program team, and offer consultation and collaboration with school staff and parents in support of student goals. CLIENT'S DESCRIPTION OF THIS OPPORTUNITY: * Assists with speech screening and initial evaluations to prepare and conduct treatment plans * Provide high-quality direct speech-language therapy services to eligible K-12 students * Conduct assessments, translate, and analyze assessment results, and develop reports to determine strengths and concerns in all communicative domains * Use professional literature, evidence-based research, and continuing education to make practice decisions * Participate in all required conferences, team meetings, and problem-solving meetings * Develop treatment plans, in conjunction with instructional staff, that are strength-based, as well as child and family-centered for overall educational improvement * Ensure evaluations, intervention plans, and service delivery are aligned with school, state, and federal guidelines * Assist and guide key stakeholders in observing, describing, and referring suspected and identified speech and language delays/disorders * Other duties as assigned CLIENT REQUIRED QUALIFICATIONS: * A Master's Degree in Speech-Language Pathology (SLP) or Communication Disorders * A valid NJ SLP license from the State of New Jersey * Current ASHA Certificate of Clinical Competence. (CCC-SLP)) Respond with your resume so we can start a conversation about our clients openings! What to expect from Kaleidoscope Education Solutions: * Exceptional compensation * Compensated weekly * Flexible schedule that meets your life's balance - Make your own schedule! * Your own personal connection in our office whenever you need support or have questions! * Grow professionally by collaborating with experienced & valued therapists * We are here for you 24/7 * Contracting with Kaleidoscope allows you all of the above and more! Imagine doing your best work in the profession you love with others who love what they do! KES ADVANTAGES * Reaching students who may otherwise go without the services they need and deserve. * Establish maximum scholastic success because of your expertise! * Develop students' maximum achievement through strong relationships with students and parents. * Instill and reinforce the joy of learning, growing and smiling. * Maintain a positive and encouraging environment. We will present you with your Best-Fit: * We partner with hundreds of schools in your area; we collaborate with you to pick the best fit! * Support: You'll have your own personal connection in our office whenever you need support or have questions! * Control Over Your Career: Working with us is like running your own business and we handle the negotiation and the details; we are here to help you succeed and work at the top of your license! * Manageable Caseloads: We advocate making sure your caseload is practical! * Balanced Workweek: We ensure you provide services for your desired hours, no more! About K.E.S. Founded in 1989, Kaleidoscope Education Solutions, Inc. started as a small team of professionals near Philadelphia, Pennsylvania. Since then, our proven results and dedication have produced exponential nationwide company growth. We additionally have offices in Arizona and service locations in Colorado, Delaware, Illinois, Massachusetts, Michigan, New Jersey, New Mexico, Pennsylvania, Texas, Utah, Washington DC, and Wisconsin. Thanks to our clients, parents, and contractors, we now work with teachers and therapists in over 5,000 schools and 700 school districts across the country! Today, Kaleidoscope Education Solutions, Inc. is a nationwide staffing leader in school-based therapy and special education services. We are dedicated to making a positive difference in every student's life.

Share this job

For security reasons, we do not recommend using the “Keep me logged in” option on public devices.

This posting is locked only for district employees, in order to apply, you need to provide a password and click "Submit".

Are you sure?

For security reasons, we do not recommend using the “Keep me logged in” option on public devices. Click Continue to move forward with stay logged in.

Reset your password

Is this your email @ ?

Enter email address to retrieve your username and/or reset your password.

Verify Your Email Address

Email verification link sent.

An Email Verification link was sent to the email address . The verification link will expire in 48 hours. Please click on the link in the email you received to continue and complete the verification process.

If you do not see the email in your inbox after approximately 10-15 minutes, check your SPAM/Junk email folder(s) , thank you.

Speech & Language Pathology Assistant at Centralia Elementary School District

Application Deadline

9/11/2024 3:30 PM Pacific

Date Posted

Number of openings, add'l salary info, length of work year, employment type, location/hours, requirements / qualifications.

Please submit required documents (only in PDF): 1) One letter of recommendation 2) Resume 3) Proof of High School diploma 4) SLPA License - Speech/Language Pathology Assistant with the State of California Speech/Language Pathology and Audiology Board. ONLY COMPLETE PACKETS WILL BE CONSIDERED

Comments and Other Information

Links related to this job.

• View Other Job Desc. / Ess. Elem.

CalPERS Links

• CalPERS Retirement Benefits

Centralia Elementary School District

Session Expiring Warning

For your safety and protection, your session is about to expire. If you wish to continue your session, please click OK .

arXiv's Accessibility Forum starts next month!

Help | Advanced Search

Computer Science > Sound

Title: enabling beam search for language model-based text-to-speech synthesis.

Abstract: Tokenising continuous speech into sequences of discrete tokens and modelling them with language models (LMs) has led to significant success in text-to-speech (TTS) synthesis. Although these models can generate speech with high quality and naturalness, their synthesised samples can still suffer from artefacts, mispronunciation, word repeating, etc. In this paper, we argue these undesirable properties could partly be caused by the randomness of sampling-based strategies during the autoregressive decoding of LMs. Therefore, we look at maximisation-based decoding approaches and propose Temporal Repetition Aware Diverse Beam Search (TRAD-BS) to find the most probable sequences of the generated speech tokens. Experiments with two state-of-the-art LM-based TTS models demonstrate that our proposed maximisation-based decoding strategy generates speech with fewer mispronunciations and improved speaker consistency.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .