Using KL-based acoustic models in a large vocabulary recognition task

Abstract

Posterior probabilities of sub-word units have been shown to be an effective front-end for ASR.However, attempts to model this type of features either do not benefit from modeling context-dependent phonemes, or use an inefficient distribution to estimate the state likelihood.This paper presents a novel acoustic model for posterior features that overcomes these limitations.The proposed model can be seen as a HMM where the score associated with each state is the KL divergence between a distribution characterizing the state and the posterior features from the test utterance.This KL-based acoustic model establishes a framework where other models for posterior features such as hybrid HMM/MLP and discrete HMM can be seen as particular cases.Experiments on the WSJ database show that the KL-based acoustic model can significantly outperform these latter approaches.Moreover, the proposed model can obtain comparable results to complex systems, such as HMM/GMM, using significantly fewer parameters.