IEEE Transactions on Audio, Speech, and Language Processing

IEEE Transactions on Audio, Speech, and Language Processing
Title	IEEE Transactions on Audio, Speech, and Language Processing
Discipline	Audio engineering; Speech processing; Natural language processing
Abbreviation	IEEE Trans. Audio Speech Lang. Process.
Publisher	IEEE Signal Processing Society
Country	United States
History	1993–present
Frequency	Monthly
Issn	1558-7916

Contents

IEEE Transactions on Audio, Speech, and Language Processing is a peer-reviewed technical journal publishing research on audio, speech, and language technologies. The journal serves a global audience of researchers and practitioners connected with Institute of Electrical and Electronics Engineers, Signal Processing Society, International Conference on Acoustics, Speech, and Signal Processing, and related conferences such as NeurIPS, ICASSP, Interspeech, and ACL.

History

The journal evolved from prior IEEE publications and committees involving figures and institutions such as Bell Labs, M.I.T. Media Lab, Stanford University, University of Cambridge, and IBM Research. Early contributors included researchers affiliated with Massachusetts Institute of Technology, Bell Labs, Carnegie Mellon University, University of Illinois Urbana–Champaign, and Harvard University. Over time editorial leadership featured editors drawn from University of California, Berkeley, University of Edinburgh, Swiss Federal Institute of Technology, Tokyo Institute of Technology, and Tsinghua University. The title and charter were influenced by meetings and policy discussions involving IEEE Signal Processing Magazine, IEEE Transactions on Signal Processing, EURASIP Journal on Audio, Speech, and Music Processing, and professional events such as the ICASSP 1995 and ICASSP 2010 proceedings.

The journal covers algorithmic, theoretical, and experimental work linked to institutions and projects like Google Research, Microsoft Research, Facebook AI Research, Amazon Alexa, and Apple Inc. research labs. Typical topics cross-link to advances reported at NeurIPS 2015, ICLR, CVPR, ACL 2018, and relate to methodologies from groups at DeepMind, OpenAI, Allen Institute for AI, and SRI International. Technical areas include acoustic signal processing developed in collaboration with NIST, speech recognition systems evaluated in shared tasks organized by LDC, speaker verification work from IARPA challenges, and language modeling traditions traced to Penn Treebank and WordNet.

Editorial governance involves an editor-in-chief supported by associate editors and an editorial board with members appointed from universities such as Princeton University, Columbia University, Delft University of Technology, ETH Zurich, University of Toronto, and corporate labs like Xerox PARC and Siemens. Peer review practices align with norms championed at Committee on Publication Ethics meetings and standards referenced by CrossRef and ORCID. The review process interfaces with submission platforms used by journals published by Wiley, Elsevier, and Springer Nature; it employs anonymized review models comparable to those used at ACL and double-blind experiments run by research groups at University of Oxford and California Institute of Technology.

The publisher, the IEEE Signal Processing Society, maintains production workflows similar to other IEEE journals such as IEEE Transactions on Signal Processing and IEEE Transactions on Neural Networks and Learning Systems. Access and licensing policies have been debated in contexts involving Plan S, SPARC, and institutional repositories at Harvard University, Stanford University Libraries, and MIT Libraries. The journal offers hybrid access options and adheres to indexing practices associated with PubMed, Scopus, and Web of Science, facilitating citation tracking alongside citation metrics curated by Clarivate Analytics and altmetrics services connected to Altmetric.com.

The journal’s impact is reflected in citation patterns alongside landmark venues such as NeurIPS, ICASSP, Interspeech, and ACL. Research published within has informed products and standards from ITU, 3GPP, W3C, and industry implementations by Sony, Samsung, Qualcomm, and NVIDIA. Scholarly reception includes recognition through awards associated with IEEE Signal Processing Society Young Author Best Paper Award and citations in influential books like those by Julius O. Smith III, Lawrence Rabiner, and B. Gold and N. Morgan. The journal is often compared with Speech Communication, Computer Speech & Language, and IEEE/ACM Transactions on Audio, Speech, and Language Processing peer venues in bibliometric analyses produced by Elsevier and Clarivate.

Notable contributions have included seminal papers on hidden Markov models linked to researchers from IBM Research, deep learning architectures from teams at Google DeepMind and Facebook AI Research, and source separation breakthroughs tied to labs at MIT, ETH Zurich, and University of Tokyo. Special issues have focused on themes promoted at conferences such as ICASSP 2016, workshops sponsored by IEEE Signal Processing Society, and collaborative initiatives with DARPA programs and CHiME challenges. Highly cited articles have influenced standards and toolkits including Kaldi, HTK, TensorFlow, and PyTorch, and have been referenced in applied projects at Amazon Robotics, Baidu Research, and Tencent AI Lab.

Category:IEEE journals Category:Audio engineering journals Category:Speech processing