|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
STATE-BASED GAUSSIAN SELECTION IN LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION USING HMMS
M. J. F. Gales, K. M. Knill and S. J. Young
This paper investigates the use of Gaussian Selection (GS) to increase the speed of a large vocabulary speech recognition system. Typically 30-70% of the computational time of a HMM-based speech recogniser is spent calculating probabilities. The aim of GS is to reduce this load by dividing the acoustic space into a set of clusters and associating a "short-list" of Gaussians with each of these clusters. Any Gaussian not in the short-list is simply approximated. This paper examines new techniques for obtaining "good" short-lists. All the new schemes make use of state information, specifically which state each of the components belongs to. In this way a maximum number of components per state may be specified, hence reducing the size of the short-list. The first technique introduced is a simple extension of the standard GS one, which uses this state information. Then, more complex schemes based on maximising the likelihood of the training data are proposed. These new approach are compared with the standard GS scheme on a large vocabulary speech recognition task. On this task, the use of state information reduced the percentage of Gaussians computed to 10-15%, compared with 20-30% for the standard GS scheme, with little degradation in performance.
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