Selective MCE training strategy in Mandarin speech recognition

The use of discriminative training methods in speech recognition is a promising approach. The minimum classification error (MCE) based discriminative methods have been extensively studied and successfully applied to speech recognition [1][2][3], speaker recognition [4], and utterance verification [5][6]. Our goal is to modify the embedded string model based MCE algorithm to train a large number of crosssyllable triphones in a large vocabulary recognition system. In this paper, selective strategy about MCE based discriminative training method, in particular for Mandarin speech syllable loop recognition, is introduced. Here, we use syllable loop recognition task to evaluate performance of the acoustic model of an established large vocabulary continuous speech recognition system. Since decoding errors only occur in parts of the whole decoded sentence, it is reasonable to adjust only the parameters of the "wrong models". As a result, we introduce the weighted MCE formulation, which can provide more effective convergence property and about 10% relative error rate reduction for a large training set. On the other hand, from our experiments, we observed that although the overall performance of recognition system is improved, some originally correct recognition results are misrecognized after discriminative training. To address this issue, we propose a divide and conquer strategy. The acoustic feature space is divided into two or more sub-spaces according to discriminative training procedure. Combining the above two methods, we got more than 14.5% error rate reduction in syllable loop recognition experiments.