Automatic generation of phonetic units for continuous speech recognition

Several techniques for dealing with the variability of recognition units are reported. The segmental k-means technique has been used for estimating two-state hidden Markov models for each of an inventory of 46 phones. Multiple models of these phones have been generated using clustering techniques intended to model separately acoustically distinct phenomena associated with the same unit. It is shown that increasing the number of models per phone can significantly increase the performance of a speaker dependent continuous speech recognizer, especially if model weights for phones which reflect their contexts in words found in the test vocabulary can be obtained. The authors feel that model splitting could be even more important in speaker dependent recognition, where it can be used to reduce model variability associated with multiple speakers as well as variable contexts. The results suggest that adequate training data must be available to train multiple models; otherwise, performance degrades when the number of models increases