Dynamic Bayesian network based speech recognition with pitch and energy as auxiliary variables

Pitch and energy are two fundamental features describing speech, having importance in human speech recognition. However, when incorporated as features in automatic speech recognition (ASR), they usually result in a significant degradation on recognition performance due to the noise inherent in estimating or modeling them. We show experimentally how this can be corrected by either conditioning the emission distributions upon these features or by marginalizing out these features in recognition. Since to do this is not obvious with standard hidden Markov models (HMMs), this work has been performed in the framework of dynamic Bayesian networks (DBNs), resulting in more flexibility in defining the topology of the emission distributions and in specifying whether variables should be marginalized out.