A mismatch-aware stochastic matching algorithm for robust speech recognition

We present a mismatch-aware stochastic matching (MASM) algorithm to alleviate the performance degradation under mismatched training and testing conditions. MASM first computes a reliability measure of applying a set of pre-trained speech models to a mismatch test utterance along the time axis or among different feature vector components. It then estimates and compensates the mismatch using the reliability measure to guide the speech segmentation. Experiments on a serious mismatched condition with training on PSTN-speech database and testing on mobile GSM-speech database showed that MASM outperformed the stochastic match (SM) method, especially, for short utterances.