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This paper investigates an adaptive noise dictionary design approach to achieve an effective and computationally feasible noise modeling for the noise robust exemplar matching (N-REM) framework. N-REM approximates noisy speech segments as a linear combination of multiple length exemplars in a sparse representation (SR) formulation. Compared to the previous SR techniques with a single overcomplete...
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 modelling noisy speech as a sparse linear combination of speech and noise exemplars. After recovering the sparsest possible linear combination of labelled exemplars, noise robust posterior likelihoods are estimated...
This paper proposes a noise robust exemplar-based speech recognition system where noisy speech is modeled as a linear combination of a set of speech and noise exemplars. The method works by finding a small number of labeled exemplars in a very large collection of speech and noise exemplars that jointly approximate the observed speech signal. We represent the exemplars using mel-energies, which allows...
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