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:
8365
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 Voice Privacy
Submission Date: 2021-01-08

Recent years have seen mounting calls for the preservation of privacy when treating personal data. Speech falls within that scope because it encapsulates a wealth of personal information that can be revealed by listening or by automatic speech analysis and recognition systems. This includes, e.g., age, gender, ethnic origin, geographical background, health or emotional state, political orientations, and religious beliefs, among others. In addition, speaker recognition systems can reveal the speaker’s identity. It is thus of no surprise that efforts to develop privacy preservation solutions for speech technology are starting to emerge. A few studies have tackled the formal definition of privacy preservation, the provision of suitable datasets, and the design of evaluation protocols and metrics based on user and attacker models. Other studies have addressed the development of privacy preservation methods which maximize the utility for users while defeating attackers. Current methods fall into four categories: deletion, encryption, anonymization, and distributed learning. Deletion methods aim to delete or obfuscate speech based on speech enhancement or privacy-preserving feature extraction for ambient sound analysis purposes. Encryption methods such as fully homomorphic encryption and secure multiparty computation can be used to implement all computations in the encrypted domain. Anonymization methods aim to suppress personal information but retain other information by means of noise addition, speech transformation, voice conversion, speech synthesis, or adversarial learning. Decentralized or federated learning methods aim to learn models (for, e.g., keyword spotting) from distributed data without accessing individual data points nor leaking information about them in the models. This special issue solicits papers describing advances in privacy protection for speech processing systems, including theoretical developments, algorithms or systems. Examples of topics relevant to the special issue include (but are not limited to): formal models of speech privacy preservation, privacy-preserving speech feature extraction, privacy-driven speech deletion or obfuscation, privacy-driven voice conversion, privacy-driven speech synthesis and transformation, privacy-preserving decentralized learning of speech models, speech processing in the encrypted domain, open resources, e.g., datasets, software or hardware implementations, evaluation recipes, objective and subjective metrics.
Last updated by Dou Sun in 2020-06-25
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