A hierarchical Bayesian model for continuous speech recognition

Abstract

This paper proposes a stochastic model for continuous speech recognition that provides automatic segmentation of a spoken utterance into phonemes and facilitates the quantitative assessment of uncertainty associated with the identified utterance features. The model is specified hierarchically within the Bayesian paradigm. At the lowest level of the hierarchy, a Gibbs distribution is used to specify a probability distribution on all the possible partitions of the utterance. The number of partitioning elements which are phonemes is not specified a priori. At a higher level in the hierarchical specification, random variables representing phoneme durations and acoustic vector values are associated reported about 0.9% word error rate. The new model was experimentally compared to continuous density mixture HMM (CDHMM) on a same recognition task, and gave significantly smaller word error rates.