Speech recognition using soft decision trees

Jitendra Ajmera, Masami Akamine

研究成果: Conference article査読

2 被引用数 (Scopus)


This paper presents recent developments at our site toward speech recognition using decision tree based acoustic models. Previously, robust decision trees have been shown to achieve better performance compared to standard Gaussian mixture model (GMM) acoustic models. This was achieved by converting hard questions (decisions) of a standard tree into soft questions using sigmoid function. In this paper, we report our work where soft-decision trees are trained from scratch. These soft-decision trees are shown to yield better speech recognition accuracy compared to standard GMM acoustic models on Aurora digit recognition task.

ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版ステータスPublished - 2008 12月 1
イベントINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
継続期間: 2008 9月 222008 9月 26

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • 感覚系


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