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Exemplar-based acoustic modeling is based on labeled training segments that are compared with the unseen test utterances with respect to a dissimilarity measure. Using a larger number of accurately labeled exemplars provides better generalization thus improved recognition performance which comes with increased computation and memory requirements. We have recently developed a noise robust exemplar...
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...
This paper describes and analyzes several exemplar selection techniques to reduce the number of exemplars that are used in a recently proposed sparse representations-based speech recognition system. Exemplars are labeled acoustic realizations of different durations which are extracted from the training data. For practical reasons, they are organized in multiple undercomplete dictionaries, each containing...
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