Feature enhancement using sparse reference and estimated soft-mask exemplar-pairs for noisy speech recognition
Lee Ngee Tan, Abeer A. Alwan · 2014
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 (SMref) and estimated soft-mask (SMest) exemplar-pairs derived from the training data. The sparse linear combination of SMestdictionary exemplars that best represents the test utterance's SMestis obtained by solving an L1-minimization problem. This sparse linear combination is applied to the SMrefexemplar 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.