Computer Speech and Language

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.4
2024	3.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.778

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	90

Journal Rank

Year	Value
2024	7045

Journal Citation Indicator

Year	Value
2024	1492

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

  Citation: 1342

  Authors: C.J., P.C.

• Maximum likelihood linear transformations for HMM-based speech recognition

  Citation: 820

  Authors: M.J.F.

• An empirical study of smoothing techniques for language modeling

  Citation: 559

  Authors: Stanley F., Joshua

• Weighted finite-state transducers in speech recognition

  Citation: 481

  Authors: Mehryar, Fernando, Michael

• The predominance of strong initial syllables in the English vocabulary

  Citation: 442

  Authors: Anne, David M.

• Voxceleb: Large-scale speaker verification in the wild

  Citation: 408

  Authors: Arsha, Joon Son, Weidi, Andrew

• Partially observable Markov decision processes for spoken dialog systems

  Citation: 398

  Authors: Jason D., Steve

• Application-independent evaluation of speaker detection

  Citation: 364

  Authors: Niko, Johan

• Support vector machines for speaker and language recognition

  Citation: 343

  Authors: W.M., J.P., D.A., E., P.A.

• Finding consensus in speech recognition: word error minimization and other applications of confusion networks

  Citation: 333

  Authors: Lidia, Eric, Andreas

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