Phonemic recognition using a large hidden Markov model

The authors present a novel method for using the state sequence output of a large hidden Markov model as input to a phonemic recognition system. It thereby demonstrates that a significant amount of speech information is preserved in the most likely state sequences produced by such a model. Two different system formulations are presented, both achieving recognitions results equivalent to those achieved by other researchers when using systems with similar levels of complexity. The best system formulation achieved a 56.1% recognition rate with 10.8% insertions on a closed-set experiment and a 53.3% recognition rate with 11.8% insertions on a speaker-independent experiment using the TIMIT acoustic-phonetic database. this experiment used 80 male speakers for model training and a separate set of 24 male speakers for model testing.<
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