Selective training for hidden Markov models with applications to speech classification

Traditional maximum likelihood estimation of hidden Markov model parameters aims at maximizing the overall probability across the training tokens of a given speech unit. As such, it disregards any interaction or biases across the models in the training procedure. Often, the resulting model parameters do not result in minimum error classification in the training set. A new selective training method is proposed that controls the influence of outliers in the training data on the generated models. The resulting models are shown to possess feature statistics which are more clearly separated for confusable patterns. The proposed selective training procedure is used for hidden Markov model training, with application to foreign accent classification, language identification, and speech recognition using the E-set alphabet. The resulting error rates are measurably improved over traditional forward-backward training under open test conditions. The proposed method is similar in terms of its goal to maximum mutual information estimation training, however it requires less computation, and the convergence properties of maximum likelihood estimation are retained in the new formulation