Graphical model architectures for speech recognition

Author

Bilmes, Jeff A. ; Bartels, Chris

Volume

22

Issue

5

fYear

2005

Firstpage

89

Lastpage

100

Abstract

This article discusses the foundations of the use of graphical models for speech recognition as presented in J. R. Deller et al. (1993), X. D. Huang et al. (2001), F. Jelinek (19970, L. R. Rabiner and B. -H. Juang (1993) and S. Young et al. (1990) giving detailed accounts of some of the more successful cases. Our discussion employs dynamic Bayesian networks (DBNs) and a DBN extension using the Graphical Model Toolkit´s (GMTK´s) basic template, a dynamic graphical model representation that is more suitable for speech and language systems. While this article concentrates on speech recognition, it should be noted that many of the ideas presented here are also applicable to natural language processing and general time-series analysis.

Keywords

belief networks; graph theory; natural languages; speech recognition; time series; dynamic Bayesian networks; dynamic graphical model representation; language systems; natural language processing; speech recognition; time-series analysis; Bayesian methods; Computer architecture; Computer science; Graphical models; Natural languages; Probability distribution; Random variables; Social network services; Speech processing; Speech recognition;

fLanguage

English

Journal_Title

Signal Processing Magazine, IEEE

Publisher

ieee

ISSN

1053-5888

Type

jour

DOI

10.1109/MSP.2005.1511827

Filename

1511827