Toward a Practical Implementation of Exemplar-Based Noise Robust ASR
In previous work it was shown that, at least in principle, an exemplar-based approach to noise robust ASR is possible. The method, Sparse representation based Classification (SC), works by modeling noisy speech as a sparse linear combination of speech and noise exemplars. After recovering the sparsest possible linear combination of labeled exemplars, noise robust posterior likelihoods are estimated by using the weights of the exemplars as evidence of the state labels underlying exemplars. Although promising recognition accuracies at low SNRs were obtained, the method was impractical due to its slow execution speed. Moreover, the performance was not as good on noisy speech corrupted by noise types not represented by the noise exemplars.