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IEEE/ACM Transactions on Audio Speech and Language Processing is a peer-reviewed journal published by IEEE, covering Speech and Audio Processing, Speech Recognition and Synthesis, Music and Audio Processing.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE/ACM Transactions on Audio Speech and Language Processing style. No sign-up. |
| Publishes research in | Speech and Audio Processing Speech Recognition and Synthesis Music and Audio Processing Advanced Adaptive Filtering Techniques Topic Modeling |
| ISSN | 2329-9290 |
| Citation impact (2-yr) | 10.7 |
| h-index | 113 |
| i10-index | 1,788 |
| Total citations | 92,465 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | dl.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Recently, the hybrid deep neural network (DNN)-hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction…
Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the…
Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised…
In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world…
Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited…
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