Effects of Bayesian predictive classification using variational Bayesian posteriors for sparse training data in speech recognition

Shinji Watanabe, Atsushi Nakamura

研究成果: Paper査読

3 被引用数 (Scopus)

抄録

We introduce a robust classification method using Bayesian predictive distribution (Bayesian predictive classification, referred to as BPC) into speech recognition. We and others have recently proposed a total Bayesian framework for speech recognition, Variational Bayesian Estimation and Clustering for speech recognition (VBEC). VBEC includes an analytical derivation of approximate posterior distributions that are essential for BPC, based on variational Bayes (VB). BPC using VB posterior distributions (VB-BPC) can mitigate the over-training effects by marginalizing output distribution. We address the sparse data problem in speech recognition, and show how VB-BPC is robust against die data sparseness, experimentally.

本文言語English
ページ1105-1108
ページ数4
出版ステータスPublished - 2005 12 1
外部発表はい
イベント9th European Conference on Speech Communication and Technology - Lisbon, Portugal
継続期間: 2005 9 42005 9 8

Conference

Conference9th European Conference on Speech Communication and Technology
CountryPortugal
CityLisbon
Period05/9/405/9/8

ASJC Scopus subject areas

  • Engineering(all)

フィンガープリント 「Effects of Bayesian predictive classification using variational Bayesian posteriors for sparse training data in speech recognition」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