DYNAMIC HMM SELECTION FOR CONTINUOUS SPEECH RECOGNITION
T. Hain & P.C. Woodland
In this paper we propose a dynamic model selection technique based on hidden model sequences (HMS). HMS modelling assumes, that not only the actual state sequence is unknown, but also the model sequence given a particular sentence. This allows more than one model to be used for a particular phone in a certain context. The most appropriate model is determined locally rather than a priori globally by the acoustic probability of that model together with a probability that this model is produced in a particular phone (or model) context. Experiments on the Resource Management corpus show significant improvements in word error rate over phonetically model-- and state--tied triphone hidden Markov models (HMMs). Initial results on the Switchboard corpus also show improvements on a much more difficult task.
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