Stochastic Vector Mapping-based Feature Enhancement Using Prior Model and Environment Adaptation for Noisy Speech Recognition

This paper presents an approach to feature enhancement for noisy speech recognition. Three prior models are introduced to characterize clean speech, noise and noisy speech respectively using sequential noise estimation based on noise-normalized stochastic vector mapping. Environment adaptation is also adopted to reduce the mismatch between training data and test data. For AURORA2 database, the experimental results indicate that a 0.77% digit accuracy improvement for multi-condition training and 0.29% digit accuracy improvement for clean speech training were achieved without stereo training data compared to the SPLICE-based approach with recursive noise estimation. For MAT-BN Mandarin broadcast news database, a 2.6% syllable accuracy improvement for anchor speech and 4.2% syllable accuracy improvement for field report speech were obtained compared to the MCE-based approach.