Journal Information
Computer Speech and Language
http://www.journals.elsevier.com/computer-speech-and-language/
Impact Factor:
1.857
Publisher:
Elsevier
ISSN:
0885-2308
Viewed:
7859
Tracked:
12

Call For Papers
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
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on State-of-the-art Handcrafted Feature Extraction for Speech and Voice Analysis
Submission Date: 2020-07-30

For over ten years, the scientific community has been witnessing a permanent rise in the number of modern feature-learning approaches for speech and voice analysis. Although those strategies have been placed at the forefront of current artificial intelligence research, uninterpretable models and high computational costs characterise their main drawbacks. Thus, the intention of this special issue is to attract the attention to the fact that, in many problems, handcrafted extraction may still provide prominent solutions with low computational costs and easy-to-interpret features. Topics of Interest The particular topics of interest are those focusing on handcrafted feature extraction approaches for speech and voice analysis. Applications include, but are not necessarily limited to: • text-dependent, text-prompted and text-independent speaker identification and verification • spoken word, limited-vocabulary and large-vocabulary speech recognition • speech emotion identification • speech characterisation • voice activity detection • idiom recognition • speech pathology detection • emerging applications, including coronavirus detection based on speech
Last updated by Dou Sun in 2020-05-25
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