Feature enhancement using sparse reference and estimated soft-mask exemplar-pairs for noisy speech recognition

A feature enhancement technique for noise-robust speech recognition is proposed. Existing sparse exemplar-based feature enhancement methods use clean speech and pure noise Mel-spectral exemplars, or clean and noisy speech log-Mel-spectral exemplar-pairs, in their dictionaries. In contrast, the proposed technique constructs its dictionaries using reference soft-mask (SM_ref) and estimated soft-mask (SM_est) exemplar-pairs derived from the training data. The sparse linear combination of SM_est dictionary exemplars that best represents the test utterance´s SM_est is obtained by solving an L1-minimization problem. This sparse linear combination is applied to the SM_ref exemplar dictionary to generate an enhanced soft-mask for denoising the utterance´s Mel-spectra before MFCC extraction. On the Aurora-2 noisy speech recognition task, the proposed algorithm outperforms other sparse Mel-spectral exemplar-based feature enhancement schemes when mismatch exists between the dictionary exemplars and the test set. A preliminary experiment on Aurora-4 shows similar trends.