Stressed speech recognition using multi-dimensional hidden Markov models

Abstract

Robust speech recognition systems must address variations due to perceptually induced stress in order to maintain acceptable levels of performance in adverse conditions. This study proposes a new approach which combines stress classification and speech recognition into one algorithm. This is accomplished by generalizing the one-dimensional hidden Markov model to a multi-dimensional hidden Markov model (N-D HMM) where each stressed speech style is allocated a dimension in the N-D HMM. It is shown that this formulation better integrates perceptually induced stress effects for stress independent recognition. This is due to the sub-phoneme (state level) stress classification that is implicitly performed by the algorithm. The proposed N-D HMM method is compared to neutral and multi-styled stress trained 1-D HMM recognizers. Average recognition rates are shown to improve by +15.72% over the 1-D stress dependent recognizer and 26.67% over the 1-D neutral trained recognizer.