Speech recognition based on student's t-distribution derived from total Bayesian framework

Shinji Watanabe*, Atsushi Nakamura

*この研究の対応する著者

研究成果: Article査読

4 被引用数 (Scopus)

抄録

We introduce a robust classification method based on the Bayesian predictive distribution (Bayesian Predictive Classification, referred to as BPC) for speech recognition. We and others have recently proposed a total Bayesian framework named Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes the practical computation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) provides an analytical solution for the predictive distribution as the Student's t-distribution, which can mitigate the over-training effects by marginalizing the model parameters of an output distribution. We address the sparse data problem in speech recognition, and show experimentally that VB-BPC is robust against data sparseness.

本文言語English
ページ(範囲)970-980
ページ数11
ジャーナルIEICE Transactions on Information and Systems
E89-D
3
DOI
出版ステータスPublished - 2006 1月 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

フィンガープリント

「Speech recognition based on student's t-distribution derived from total Bayesian framework」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル