IEEE/ACM Transactions on Audio Speech and Language Processing

Aims & Scope

The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them.

In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio.

In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement.

In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	5.1
2024	4.10

Journal Rank

Year	Value
2024	4391

Journal Citation Indicator

Year	Value
2024	6788

SJR (SCImago Journal Rank)

Year	Value
2024	1.061

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	92

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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, Engineering, Health Professions, Mathematics and Physics and Astronomy, 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.

• Convolutional Neural Networks for Speech Recognition

  Citation: 1759

  Authors: Ossama, Abdel-rahman, Hui, Li, Gerald, Dong

• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

  Citation: 1659

  Authors: Wei-Ning, Benjamin, Yao-Hung Hubert, Kushal, Ruslan, Abdelrahman

• Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

  Citation: 1396

  Authors: Yi, Nima

• Supervised Speech Separation Based on Deep Learning: An Overview

  Citation: 1074

  Authors: DeLiang, Jitong

• A Regression Approach to Speech Enhancement Based on Deep Neural Networks

  Citation: 974

  Authors: Yong, Jun, Li-Rong, Chin-Hui

• Pre-Training With Whole Word Masking for Chinese BERT

  Citation: 888

  Authors: Yiming, Wanxiang, Ting, Bing, Ziqing

• On Training Targets for Supervised Speech Separation

  Citation: 789

  Authors: Arun

• PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

  Citation: 766

  Authors: Qiuqiang, Yin, Turab, Yuxuan, Wenwu, Mark D.

• Complex Ratio Masking for Monaural Speech Separation

  Citation: 605

  Authors: Donald S., Yuxuan, DeLiang

• Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks

  Citation: 580

  Authors: Morten, Dong, Zheng-Hua, Jesper

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