Discriminative simplification of mixture models

Yossi Bar-Yosef, Yuval Bistritz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Simplification of mixture models has recently emerged as an important issue in the field of statistical learning. The heavy computational demands of using large order models drove researches to investigate how to efficiently reduce the number of components in mixture models. The simplification, in solutions proposed so far, was performed by maximizing a certain measure of similarity to the original model, regardless of the discriminative qualities among models of different classes. This paper proposes a novel discriminative learning algorithm for reducing the order of a set of mixture models. The suggested algorithm is based on maximizing the correct component association. Experiments, performed on acoustic modeling in a basic phone recognition task, indicate that the proposed algorithm outperforms the comparable non-discriminative simplification algorithm.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2240-2243
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Gaussian mixture models
  • discriminative learning
  • hierarchical clustering
  • phone recognition

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