Gesture-based Dynamic Bayesian Network for noise robust speech recognition

Previously we have proposed different models for estimating articulatory gestures and vocal tract variable (TV) trajectories from synthetic speech. We have shown that when deployed on natural speech, such models can help to improve the noise robustness of a hidden Markov model (HMM) based speech recognition system. In this paper we propose a model for estimating TVs trained on natural speech and present a Dynamic Bayesian Network (DBN) based speech recognition architecture that treats vocal tract constriction gestures as hidden variables, eliminating the necessity for explicit gesture recognition. Using the proposed architecture we performed a word recognition task for the noisy data of Aurora 2. Significant improvement was observed in using the gestural information as hidden variables in a DBN architecture over using only the mel-frequency cepstral coefficient based HMM or DBN backend. We also compare our results with other noise-robust front ends.