Hidden Markov models using vector linear prediction and discriminative output distributions

HMMs model signal dynamics rather poorly. Modeling accuracy can be improved by adding vector linear predictors to each state in order to predict the value of the current observation based on correlations with nearby observations. A vector linear predictive HMM is discussed and re-estimation formulae for the predictor parameters presented. Multiple speaker recognition experiments on a 104 talker British English E-set database were performed to test the method on a difficult speech recognition task. It was found that a baseline test set error rate of 5.6% improved to 4.1% using a single diagonal predictor. Further improvements in performance along with a reduction in computation were obtained by using the method of discriminative output distributions on the prediction error. This resulted in a best test set error rate of 2.8% from a system that required only half the computation of the baseline.<
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