Detail publikace

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.

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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",
  annote="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.",
  address="Elsevier Science",
  booktitle="Computer Speech & Language, Volume 25, Issue 2, April 2011",
  chapter="76383",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Elsevier Science",
  number="2",
  volume="25",
  year="2011",
  month="april",
  pages="404--439",
  publisher="Elsevier Science",
  type="journal article in Web of Science"
}