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.
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