期刊信息

Computer Speech & Language

CCF: C 影响因子: 3.0
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出版商:
Elsevier
ISSN:
0885-2308
浏览:
34177
关注:
30
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征稿

Computer Speech & Language is an academic journal published by Elsevier. (ISSN 0885-2308, impact factor 3.0, CCF C).

An official publication of the International Speech Communication Association (ISCA) Computer Speech & Language publishes reports of original research related to the analysis, recognition, understanding, production, synthesis, coding and mining of speech and spoken language, including spoken language dialogue systems and speech-based interaction with ‘intelligent’ artefacts, and their wide-ranging applications. The journal provides a focus for this work, and encourages an interdisciplinary approach to speech and spoken language research and technology. Thus contributions from all of the related fields are welcomed in the form of reports of theoretical or experimental studies, reviews, and 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 Computational models of spoken language discourse and dialogue Speech and spoken-language inclusive multimodal approaches and systems Information retrieval, extraction and summarization of spoken language media Speaker and spoken language recognition Computational models of speech production and perception Signal processing for speech analysis, enhancement and transformation Evaluation of speech-based interactive systems Applications of speech and spoken language technologies Note that we are no longer accepting submissions devoted to pure Natural Language Processing NLP (i.e. all new manuscripts must address some aspect of spoken language processing).
由 Dou Sun 最后更新于

特刊

特刊：Acoustical Intelligence in the Wild: Advancing Acoustic Signal Understanding in Open Environments 截稿日期: 2027-05-31 The last decade has witnessed remarkable progress in speech and audio signal understanding. However, most state-of-the-art models are trained on static datasets under a "closed-set" assumption. In contrast, the real-world acoustic environment is inherently dynamic and continuously evolving. A truly intelligent system must not only recognize known sounds but also adapt to new speech patterns, novel acoustic events, and changing background conditions over time without catastrophic forgetting. This Special Issue focuses on the frontier of Continual and Life-long Learning for Speech and Audio. When deployed "in the wild," systems encounter a stream of out-of-distribution (OOD) data. The challenge lies in transitioning from static inference to dynamic adaptation: enabling models to incrementally learn from new acoustic environments while maintaining robustness on previously mastered tasks. This is particularly critical for personalized voice assistants, longterm environmental monitoring, and adaptive hearing aids where the acoustic distribution shifts daily. We invite original research that addresses the lifecycle of acoustic models in the wild. Key topics include, but are not limited to: Class-Incremental Learning for New Sound Events and Spoken Languages. Online Domain Adaptation for Speech Enhancement in Non-stationary Noise. Mitigating Catastrophic Forgetting in Large-scale Audio Pre-trained Models. Open-set Detection and Active Learning for Unlabeled Acoustic Streams. Memory-efficient Continual Learning for Edge Audio Devices. Privacy-preserving Life-long Learning for Personalized Speech Interfaces. By bridging the gap between continual machine learning and audio signal processing, this issue seeks to establish a new paradigm for "Living" acoustic systems—models that grow and refine their understanding alongside the ever-changing soundscapes they inhabit. Guest editors: Prof. Kele Xu (Executive Guest Editor) National University of Defense Technology, Changsha, China Email:[email protected] Research Interests: Computer audition, Multimodal machine learning Dr. Boqing Zhu National University of Defense Technology, Changsha, China Email: [email protected] Research Interests: Continual Leaning, acoustic Signal Processing, and Underwater Acoustic Prof. Qian Kun Beijing Institute of Technology, Beijing, China Email: [email protected] Research Interests: Computer Audition, Signal Processing, Healthcare Prof. Zixing Zhang Hunan University, Changsha, China Email: [email protected] Manuscript submission information: Important Dates: Submission Open Date: April 15, 2026 Submission Deadline: May 31, 2027 Editorial Acceptance Deadline: June 30, 2027 Submission Guidelines: All manuscripts should be submitted electronically through Editorial Manager® at https://www.editorialmanager.com/ycsla/default.aspx. When submitting papers, please select the Article Type as "VSI: Acoustical Intelligence in the Wild". Authors should prepare their manuscripts according to the "Guide for Authors" of the Computer Speech & Language outlined at the journal website. All papers will be peer-reviewed following a regular reviewing procedure. For any further information, the authors may contact the Guest Editors. Keywords: Continual Learning; Robust Speech Processing; Open-World Acoustic Intelligence; Life-long Machine Audition; Non-stationary Signal Understanding https://www.sciencedirect.com/special-issue/332571/acoustical-intelligence-in-the-wild-advancing-acoustic-signal-understanding-in-open-environments
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CCF全称影响因子出版商ISSN
BNeural Networks7.2Elsevier0893-6080

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