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IEEE 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. Over its lifetime it has published 2,101 papers, which have received 109,564 citations.
| 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 Transactions on Audio, Speech and Language Processing, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Audio, Speech and Language Processing style. No sign-up. |
| Abbreviation (ISO-4) | TASLP |
| Publishes research in | Speech and Audio Processing Speech Recognition and Synthesis Music and Audio Processing Advanced Adaptive Filtering Techniques Blind Source Separation Techniques |
| ISSN | 1558-7916 |
| h-index | 143 |
| i10-index | 1,356 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | ieeexplore.ieee.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.
This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems…
We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep…
In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated…
In the development process of noise-reduction algorithms, an objective machine-driven intelligibility measure which shows high correlation with speech intelligibility is of great interest. Besides reducing time and costs compared to real listening experiments, an objective intelligibility measure could also help provide answers on how to improve the intelligibility of noisy unprocessed speech. In this paper,…
In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive,…
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