HMM-based speech recognition using decision trees instead of GMMs

Remco Teunen, Masami Akamine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we experiment with decision trees as replacements for Gaussian mixture models to compute the observation likelihoods for a given HMM state in a speech recognition system. Decision trees have a number of advantageous properties, such as that they do not impose restrictions on the number or types of features, and that they automatically perform feature selection. In fact, due to the conditional nature of the decision tree evaluation process, the subset of features that is actually used during recognition depends on the input signal. Automatic state-tying can be incorporated directly into the acoustic model as well, and it too becomes a function of the input signal. Experimental results for the Aurora 2 speech database show that a system using decision trees offers state-of-the-art performance, even without taking advantage of its full potential.

Original languageEnglish
Title of host publicationInternational Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007
Pages617-620
Number of pages4
Publication statusPublished - 2007 Dec 1
Externally publishedYes
Event8th Annual Conference of the International Speech Communication Association, Interspeech 2007 - Antwerp, Belgium
Duration: 2007 Aug 272007 Aug 31

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume1
ISSN (Electronic)1990-9772

Other

Other8th Annual Conference of the International Speech Communication Association, Interspeech 2007
CountryBelgium
CityAntwerp
Period07/8/2707/8/31

Keywords

  • Acoustic modeling
  • Decision trees
  • Likelihood computation
  • Probability estimation
  • Speech recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Modelling and Simulation
  • Linguistics and Language
  • Communication

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  • Cite this

    Teunen, R., & Akamine, M. (2007). HMM-based speech recognition using decision trees instead of GMMs. In International Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007 (pp. 617-620). (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH; Vol. 1).