Faculty of Information Technology, BUT

Publication Details

The subspace Gaussian mixture model-A structured model for speech recognition

POVEY Daniel, BURGET Lukáš, AGARWAL Mohit, AKYAZI Pinar, GHOSHAL Arnab, GLEMBEK Ondřej, GOEL Nagendra K., KARAFIÁT Martin, RASTROW Ariya, ROSE Richard, SCHWARZ Petr and THOMAS Samuel et al. The subspace Gaussian mixture model-A structured model for speech recognition. Computer Speech and Language, vol. 25, no. 2, pp. 404-439. ISSN 0885-2308.
Czech title
Sub-space gaussovský model - strukturovaný model pro rozpoznávání řeči
Type
journal article
Language
english
Authors
Povey Daniel (JHU)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Agarwal Mohit (IIIT)
Akyazi Pinar (UBOGAZ)
Ghoshal Arnab (UEDIN)
Glembek Ondřej, Ing., Ph.D. (DCGM FIT BUT)
Goel Nagendra K. (GOVIVACE)
Karafiát Martin, Ing., Ph.D. (DCGM FIT BUT)
Rastrow Ariya (JHU)
Rose Richard (MCGILL)
Schwarz Petr, Ing., Ph.D. (DCGM FIT BUT)
and others
URL
Keywords
Speech recognition; Gaussian Mixture Model; Subspace Gaussian Mixture Model
Abstract
Speech recognition based on the Hidden Markov Model-Gaussian Mixture Model (HMM-GMM) framework generally involves training a completely separate GMM in each HMM state.We introduce a model in which the HMM states share a common structure but the means and mixture weights are allowed to vary in a subspace of the full parameter space, controlled by a global mapping from a vector space to the space of GMM parameters.
Annotation
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.
Published
2011
Pages
404-439
Journal
Computer Speech and Language, vol. 25, no. 2, ISSN 0885-2308
Book
Computer Speech & Language, Volume 25, Issue 2, April 2011
Publisher
Elsevier Science
BibTeX
@ARTICLE{FITPUB9670,
   author = "Daniel Povey and Luk\'{a}\v{s} Burget and Mohit Agarwal and Pinar Akyazi and Arnab Ghoshal and Ond\v{r}ej Glembek and K. Nagendra Goel and Martin Karafi\'{a}t and Ariya Rastrow and Richard Rose and Petr Schwarz and Samuel Thomas and et al.",
   title = "The subspace Gaussian mixture model-A structured model for speech recognition",
   pages = "404--439",
   booktitle = "Computer Speech \& Language, Volume 25, Issue 2, April 2011",
   journal = "Computer Speech and Language",
   volume = 25,
   number = 2,
   year = 2011,
   publisher = "Elsevier Science",
   ISSN = "0885-2308",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/9670"
}
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