|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
VECTOR QUANTIZATION BIGRAM HIDDEN MARKOV MODELLING FOR IMPROVED PHONEME RECOGNITION
G. Wong and S. J. Young
The development of accurate and robust phonetic models is essential for high-performance continuous speech recognition since the words themselves are mapped out as a sequence of phonemes. One approach is to model the time dependencies of the acoustic features in a phoneme more accurately. Short-time correlation between successive feature vectors (condensed as vector quantization codes) is modelled as discrete emission probabilities embedded in the observation process of a Hidden Markov Model (HMM). Reestimation equations in an Expectation-Maximization (EM) framework are presented for the training of such a model, as well as the Viterbi decoding algorithm necessary for phoneme based continuous speech recognition. The Expectation step in the parameter reestimation stage calculates the log likelihood of the observation sequence and the Maximization step yields the estimates of the state transition terms and conditional output pdf parameters separately. A Lagrange interpretation of the derived reestimation formulas is also presented. Recognition results using the TIMIT database are compared with conventional discrete Hidden Markov modelling methods and a measurable improvement (14\% error rate reduction) has been achieved. Implementation and several aspects of this modelling method are discussed with possible extensions for further improvements.
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