Publication detail

A Symmetrization of the Subspace Gaussian Mixture Model

POVEY, D. KARAFIÁT, M. GHOSHAL, A. SCHWARZ, P.

Original Title

A Symmetrization of the Subspace Gaussian Mixture Model

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

We have described a modification to the Subspace Gaussian Mixture Model which we call the Symmetric SGMM. This is a very natural extension which removes an asymmetry in the way the Gaussian mixture weights were previously computed. The extra computation is minimal but the memory used for the acoustic model is nearly doubled. Our experimental results were inconsistent: on one setup we got a large improvement of 1.5% absolute, and on another setup it was much smaller.

Keywords

Speech Recognition, Hidden Markov Models, Subspace Gaussian Mixture Models

Authors

POVEY, D.; KARAFIÁT, M.; GHOSHAL, A.; SCHWARZ, P.

RIV year

2011

Released

22. 5. 2011

Publisher

IEEE Signal Processing Society

Location

Praha

ISBN

978-1-4577-0537-3

Book

Proceedings of 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing

Pages from

4504

Pages to

4507

Pages count

4

URL

http://www.fit.vutbr.cz/research/groups/speech/publi/2011/povey_icassp2011_4504.pdf

BibTex

@inproceedings{BUT76375,
  author="Daniel {Povey} and Martin {Karafiát} and Arnab {Ghoshal} and Petr {Schwarz}",
  title="A Symmetrization of the Subspace Gaussian Mixture Model",
  booktitle="Proceedings of 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing",
  year="2011",
  pages="4504--4507",
  publisher="IEEE Signal Processing Society",
  address="Praha",
  isbn="978-1-4577-0537-3",
  url="http://www.fit.vutbr.cz/research/groups/speech/publi/2011/povey_icassp2011_4504.pdf"
}