A Segment Based Approach for Prosodic Boundary Detection
TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
An algorithm for the dynamic inference of hidden Markov models (DIHMM)
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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A technique for constructing hidden Markov models for the acoustic representation of words is described. The models, built from combinations of acoustically based subword units called fenones, are derived automatically from one or more sample utterances of words. They are more flexible than previously reported fenone-based word models and lead to an improved capability of modeling variations in pronunciation. In addition, their construction is simplified, because it can be done using the well-known forward-backward algorithm for the parameter estimation of hidden Markov models. Experimental results obtained on a 5000-word vocabulary continuous speech recognition task are presented to illustrate some of the benefits associated with the new models. Multonic baseforms resulted in a reduction of 16% in the average error rate obtained for ten speakers.