Speaker: Dr Shinji Watanabe
         NTT laboratories, Japan
Title: Variational Bayesian Estimation and Clustering for 
       speech recognition (VBEC)

In this task, I will introduce our proposal Variational Bayesian
Estimation and Clustering for speech recognition (VBEC), which is
based on the Variational Bayesian (VB) approach. VBEC is a total
Bayesian framework, i.e., all speech recognition procedures (acoustic
modeling and speech classification) are based on VB posterior
distribution, unlike the Maximum Likelihood (ML) approach based on ML
parameters. The total Bayesian framework generates two major Bayesian
advantages over the ML approach for the mitigation of over-training
effects, as it can select an appropriate model structure without any
data set size condition, and can classify categories robustly using a
predictive posterior distribution. By using these advantages, VBEC
(1) allows the automatic construction of acoustic models along two
separate dimensions, namely, clustering triphone Hidden Markov Model
states and determining the number of Gaussians, and (2) enables
robust speech classification, based on Bayesian Predictive
Classification using VB posterior distributions (VB-BPC). The
capabilities of the VBEC functions were confirmed in large vocabulary
continuous speech recognition experiments for read and spontaneous
speech tasks. The experiments confirmed that VBEC automatically
constructed accurate acoustic models and robustly classified speech,
i.e., totally mitigated the over-training effects with high word
accuracy due to the VBEC functions.