HMM-based speech recognition using decision trees instead of GMMs

Remco Teunen, Masami Akamine

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルInternational Speech Communication Association - 8th Annual Conference of the International Speech Communication Association, Interspeech 2007
ページ617-620
ページ数4
出版ステータスPublished - 2007
外部発表はい
イベント8th Annual Conference of the International Speech Communication Association, Interspeech 2007 - Antwerp, Belgium
継続期間: 2007 8月 272007 8月 31

出版物シリーズ

名前Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
1
ISSN(電子版)1990-9772

Other

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

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • モデリングとシミュレーション
  • 言語学および言語
  • 通信

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