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IEEE Transactions on Speech and Audio Processing is a peer-reviewed journal published by IEEE, covering Speech and Audio Processing, Speech Recognition and Synthesis, Advanced Adaptive Filtering Techniques.
| 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 Speech and Audio Processing, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Speech and Audio Processing style. No sign-up. |
| Publishes research in | Speech and Audio Processing Speech Recognition and Synthesis Advanced Adaptive Filtering Techniques Music and Audio Processing Advanced Data Compression Techniques |
| ISSN | 1063-6676 |
| h-index | 141 |
| i10-index | 729 |
| Total citations | 81,094 |
| Top institutions publishing here | AT&T (United States) |
| 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 introduces and motivates the use of Gaussian mixture models (GMM) for robust text-independent speaker identification. The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity. The focus of this work is on applications which require high identification rates using short…
Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available…
In this paper, a framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Three key issues of MAP estimation, namely, the choice of prior distribution family, the specification of the parameters of prior densities, and the evaluation of the MAP estimates, are addressed. Using HMM's with Gaussian mixture state observation…
Performance of even the best current stochastic recognizers severely degrades in an unexpected communications environment. In some cases, the environmental effect can be modeled by a set of simple transformations and, in particular, by convolution with an environmental impulse response and the addition of some environmental noise. Often, the temporal properties of these environmental effects…
We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks…
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