Fast and accurate acoustic modelling with semi-continuous HMMs

Abstract

In this paper the design of accurate Semi-Continuous Density Hidden Markov Models (SC-HMMs) for acoustic modelling in large vocabulary continuous speech recognition is presented. Two methods are described to improve drastically the efficiency of the observation likelihood calculations for the SC-HMMs. First, reduced SC-HMMs are created, where each state does not share all the – gaussian – probability density functions ( pdfs) but only those which are important for it. It is shown how the average number of gaussians per state can be reduced to 70 for a total set of 10 000 gaussians. Second, a novel scalar selection algorithm is presented reducing to 5% the number of gaussians which have to be calculated on the total set of 10 000, without any degradation in recognition performance. Furthermore, the concept of tied state context-dependent modelling with phonetic decision trees is adapted to SC-HMMs. In fact, a node splitting criterion appropriate for SC-HMMs is introduced: it is based on a distance measure between the mixtures of gaussian pdfs as involved in SC-HMM state modelling. This contrasts with other criteria from literature which are based on simplified pdfs to manage the algorithmic complexity. On the ARPA Resource Management task, a relative reduction in word error rate of 8% was achieved with the proposed criterion, comparing with two known criteria based on simplified pdfs.