Sub-Space Missing Feature Imputation and Environment Sniffing for Robust Speech Recognition

Noise robustness is the most important issue for real-life speech recognition/spoken dialogue system. In this paper, a sub-space missing feature theory (S-MFT) front-end is proposed to alleviate the corruption of the background noise. SMFT incorporates temporal information and applies principle component analysis (PCA) to find a noise-suppressed subspace for more precise missing feature imputation. Moreover, two parameters including signal-to-noise ratio (SNR) and divergence-based reliability measure of an input utterance generated by S-MFT are employed to judge whether the utterance can be successfully recognized or not. Experiments on TIDIGIT and three kinds of noises from NOISEX-92 corpora have verified the benefits of the proposed S-MFT approach. The average recognition rate could be improved from 61.6% (cepstral mean and variance normalization, CN) to 77.1% and further to 89.9% while rejecting bad utterances.