Publication detail

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

POVEY, D. BURGET, L. AGARWAL, M. AKYAZI, P. GHOSHAL, A. GLEMBEK, O. GOEL, N. KARAFIÁT, M. RASTROW, A. ROSE, R. SCHWARZ, P. THOMAS, S.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Speech recognition; Gaussian Mixture Model; Subspace Gaussian Mixture Model

Authors

POVEY, D.; BURGET, L.; AGARWAL, M.; AKYAZI, P.; GHOSHAL, A.; GLEMBEK, O.; GOEL, N.; KARAFIÁT, M.; RASTROW, A.; ROSE, R.; SCHWARZ, P.; THOMAS, S.

RIV year

2011

Released

1. 4. 2011

Publisher

Elsevier Science

ISBN

0885-2308

Periodical

COMPUTER SPEECH AND LANGUAGE

Year of study

25

Number

2

State

United Kingdom of Great Britain and Northern Ireland

Pages from

404

Pages to

439

Pages count

36

URL

https://www.fit.vut.cz/research/publication/9670/

BibTex

@article{BUT76383,
  author="Daniel {Povey} and Lukáš {Burget} and Mohit {Agarwal} and Pinar {Akyazi} and Arnab {Ghoshal} and Ondřej {Glembek} and Nagendra {Goel} and Martin {Karafiát} and Ariya {Rastrow} and Richard {Rose} and Petr {Schwarz} and Samuel {Thomas}",
  title="The subspace Gaussian mixture model-A structured model for speech recognition",
  journal="COMPUTER SPEECH AND LANGUAGE",
  year="2011",
  volume="25",
  number="2",
  pages="404--439",
  issn="0885-2308",
  url="https://www.fit.vut.cz/research/publication/9670/"
}