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Using Sparse Classification Outputs as Feature Observations for Noise-robust ASR

Sparse Classification (SC) is an exemplar-based approach to Automatic Speech Recognition. By representing noisy speech as a sparse linear combination of speech and noise exemplars, SC allows separating speech from noise. The approach has shown its robustness in noisy conditions, but at the cost of degradation in clean conditions. In this work, rather than using the state probability estimates obtained with SC directly in a Viterbi decoding, the probability distributions of SC are modeled by Gaussian Mixture Models (GMMs), for which purpose we introduce a novel transformation. Results on the AURORA-2 task show that our proposed approach is effective in all high SNR conditions in both test set A and B. We achieve a word error rate reduction of 47.4% and 29.9% averaged cross 0-20dB SNR in test set A and B respectively. The reduction rate at clean goes up to 70.6% relative to the SC baseline.