Abstract

Today, most of the state-of-the-art speech recognizers are based on Hidden Markov modeling. Using semi-continuous or continuous density Hidden Markov Models, the computation of emission probabilities requires the evaluation of mixture Gaussian probability density functions. Since it is very expensive to evaluate all the Gaussians of the mixture density codebook, many recognizers only compute the M most significant Gaussians (M = 1; : : : ; 8). This paper presents an alternative approach to approximate mixture Gaussians with diagonal covariance matrices, based on a binary feature space partitioning tree. The proposed algorithm is experimentally evaluated in the context of large vocabulary, speaker independent, spontaneous speech recognition using the JANUS-2 speech recognizer. In the case of mixtures with 50 Gaussians, we achieve a speedup of 2-5 in the computation of HMM emission probabilities, without affecting the accuracy of the system. 1. INTRODUCTION To approximate the log probab...

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