Simplified neural network architectures for a hybrid speech recognition system with small vocabulary size

Recent studies suggest that a hybrid speech recognition system based on a hidden Markov model (HMM) with a neural network (NN) subsystem as the estimator of the state conditional observation probability may have some advantages over the conventional HMMs with Gaussian mixture models for the observation probabilities. The HMM and NN modules are typically treated as separate entities in a hybrid system. This paper, however, suggests that the a priori knowledge of HMM structure can be beneficial in the design of the NN subsystem. A case of isolated word recognition is studied to demonstrate that a substantially simplified NN can be achieved in a structured HMM by applying a Bayesian factorization and pre-classification. The results indicate a similar performance to that obtained with the classical approach with much less complexity in NN structure.