computer speech and language elsevier

Computer Speech and Language

Subject Area and Category

• Human-Computer Interaction
• Theoretical Computer Science

Academic Press

Publication type

08852308, 10958363

1986-1987, 1989-2024

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Computer Speech And Language

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NLP期刊-Computer Speech and Language

Computer Speech and Language

Computer Speech and Language (CSL) 是出版商Elsevier旗下专注recognition, understanding, production, coding and mining of speech and language的刊物。

刊物的主题，editors in chief, special issue都可以在主页看到。

CSL属于CCF C类期刊，最新的影响因子为3.3，cite score 为6.7。

个人感觉CSL审稿质量十分不错，reviewer大多比较慎重，给出的意见较为合理。

由于期刊的特点，CSL一审的decision较少有直接拒稿。如果后续的revised version能够较大程度地解决审稿人的comments，可以很大的提升接受的概率。

CSL期刊的节奏算是比较快的，审稿人有大概3周时间对投稿进行review。和其他期刊相比，CSL一审的周期可以保证在两个月左右，和一般的NLP会议大致持平。

当然由于很多投稿不能在第一轮审稿中被接受，在计划投稿之前需要将二审的时间考虑进去。加上二审的时间，从投稿到收到最终通知大概需要6-8个月。

Special Issue 特别征稿

如果经常关注CSL期刊，会发现CSL经常有特别征稿。特别征稿的特点是特定主题，非双盲评阅，且审稿周期更快。

如果手上的工作和特别征稿的主题相符，那么选择special issue是十分不错的选择。

特别征稿的信息可以在最上面的链接里看到。

首先访问该刊物的editorial manager page，并作为author登陆：

第一次登陆投稿系统需要注册author账号。

登陆后在author main menu进入submit new manuscript页面。

选择文章类型，上传文章的pdf文件。

甚至可以自行选择 reviewer：reviewer preference

投稿完成后会收到确认邮件。

收到审稿邀请邮件之后，可以直接点击邀请链接。

审稿form和大多数NLP会议和期刊都差不多，没啥好说的。

审稿结束了会送你Elsevier数据库免费访问礼品卡，不过大概率没啥用，机构大多都有权限的 。

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Computer Speech and Language

Summary: Computer Speech and Language covers all topics concerning speech and language.

Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language.

The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.

The journal provides a focus for this work, and encourages an interdisciplinary approach to speech and language research and technology. Thus contributions from all of the related fields are welcomed in the form of reports of theoretical or experimental studies, tutorials, and brief correspondence pertaining to models and their implementation, or reports of fundamental research leading to the improvement of such models.

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• Computer Speech and Language

Journal Title

• E 1095-8363 | P 0885-2308 | 1095-8363 | 0885-2308
• Academic Press

Listed on (Coverage)

based on the information

• SCOPUS:2020-10

Aime & Scopes

• Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology. The journal provides a focus for this work, and encourages an interdisciplinary approach to speech and language research and technology. Thus contributions from all of the related fields are welcomed in the form of reports of theoretical or experimental studies, tutorials, reviews, and brief correspondence pertaining to models and their implementation, or reports of fundamental research leading to the improvement of such models. Research Areas Include /// Algorithms and models for speech recognition and synthesis /// Natural language processing for speech understanding and generation /// Statistical computational linguistics /// Computational models of discourse and dialogue /// Information retrieval, extraction and summarization /// Speaker and language recognition /// Computational models of speech production and perception /// Signal processing for speech analysis, enhancement and transformation /// Evaluation of human and computer system performance
• https://www.sciencedirect.com/journal/computer-speech-and-language

Article List

1 - 2 out of 2 results.

Jang, Myeongjun;  Kang, Pilsung

Lee, Taewoo; Chang, Sukmoon; Yook, Dongsuk

RSS_1.0 RSS_2.0 ATOM_1.0

145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea +82-2-3290-2963

COPYRIGHT © 2021 Korea University. All Rights Reserved.

Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.

• Researchers

Computer Speech and Language (Journal)

• Schuppler, B. (Peer reviewer)
• Institute of Signal Processing and Speech Communication (4420)

Activity : Publication peer-review or editorial work › Editorial activity

Computer Speech and Language Impact Factor & Key Scientometrics

Computer speech and language overview, impact factor.

I. Basic Journal Info

Journal ISSN: 08852308, 10958363

Publisher: elsevier inc., history: 1986-1987, 1989-ongoing, journal hompage: link, how to get published:, research categories, scope/description:.

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II. Science Citation Report (SCR)

Computer speech and language scr impact factor, computer speech and language scr journal ranking, computer speech and language scimago sjr rank.

SCImago Journal Rank (SJR indicator) is a measure of scientific influence of scholarly journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from.

Computer Speech and Language Scopus 2-Year Impact Factor Trend

Computer speech and language scopus 3-year impact factor trend, computer speech and language scopus 4-year impact factor trend, computer speech and language impact factor history.

• 2022 Impact Factor 5.385 4.703 4.315
• 2021 Impact Factor 4.479 3.95 4.044
• 2020 Impact Factor 2.73 3.262 2.922
• 2019 Impact Factor 3.901 3.382 3.415
• 2018 Impact Factor 2.862 3.019 3.121
• 2017 Impact Factor 2.975 3.217 3.041
• 2016 Impact Factor 3.13 3.068 2.956
• 2015 Impact Factor 3.253 3.154 3.192
• 2014 Impact Factor 2.615 NA NA
• 2013 Impact Factor 3.134 NA NA
• 2012 Impact Factor 3.86 NA NA
• 2011 Impact Factor 3.729 NA NA
• 2010 Impact Factor 3.02 NA NA
• 2009 Impact Factor 2.672 NA NA
• 2008 Impact Factor 3.569 NA NA
• 2007 Impact Factor 3 NA NA
• 2006 Impact Factor 1.766 NA NA
• 2005 Impact Factor 1.22 NA NA
• 2004 Impact Factor 2.488 NA NA
• 2003 Impact Factor 2.146 NA NA
• 2002 Impact Factor 1.167 NA NA
• 2001 Impact Factor 1.541 NA NA
• 2000 Impact Factor 1.659 NA NA

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Impact factor (IF) is a scientometric factor based on the yearly average number of citations on articles published by a particular journal in the last two years. A journal impact factor is frequently used as a proxy for the relative importance of a journal within its field. Find out more: What is a good impact factor?

III. Other Science Influence Indicators

Any impact factor or scientometric indicator alone will not give you the full picture of a science journal. There are also other factors such as H-Index, Self-Citation Ratio, SJR, SNIP, etc. Researchers may also consider the practical aspect of a journal such as publication fees, acceptance rate, review speed. ( Learn More )

Computer Speech and Language H-Index

The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications

Computer Speech and Language H-Index History

scijournal.org is a platform dedicated to making the search and use of impact factors of science journals easier.

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Computer Speech and Language

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Computer Science > Computation and Language

Title: code-mixed sentiment and hate-speech prediction.

Abstract: Code-mixed discourse combines multiple languages in a single text. It is commonly used in informal discourse in countries with several official languages, but also in many other countries in combination with English or neighboring languages. As recently large language models have dominated most natural language processing tasks, we investigated their performance in code-mixed settings for relevant tasks. We first created four new bilingual pre-trained masked language models for English-Hindi and English-Slovene languages, specifically aimed to support informal language. Then we performed an evaluation of monolingual, bilingual, few-lingual, and massively multilingual models on several languages, using two tasks that frequently contain code-mixed text, in particular, sentiment analysis and offensive language detection in social media texts. The results show that the most successful classifiers are fine-tuned bilingual models and multilingual models, specialized for social media texts, followed by non-specialized massively multilingual and monolingual models, while huge generative models are not competitive. For our affective problems, the models mostly perform slightly better on code-mixed data compared to non-code-mixed data.

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