Improved HMM training and scoring strategies with application to accent classification

We propose two methods to improve HMM speech recognition performance. The first method employs an adjustment in the training stage, whereas the second method employs it in the scoring stage. It is well known that a speech recognition system performance increases when the amount of labeled training data is large. However, due to factors such as inaccurate phonetic labeling, end-point detection, and voiced-unvoiced decisions, the labeling procedure can be prone to errors. We propose a selective hidden Markov model (HMM) training procedure in order to reduce the adverse influence of atypical training data on the generated models. To demonstrate its usefulness, selective training is applied to the problem of accent classification, resulting in a 9.4% improvement in classification error rate. The second goal is to improve HMM scoring performance. The objective of HMM training algorithms is to maximize the probability over the training tokens for each model. However, this does not guarantee a minimized error rate across the entire model set. Typically, biases in the confusion matrices can be observed. We propose a method for estimating the bias from input training data, and incorporating it into the general scoring algorithm. Using this technique, a 9.8% improvement is achieved in accent classification error rate