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In this paper, a new fast compressive sensing (CS) algorithm for phoneme classification is introduced. In this approach, unlike common CS classification approaches that use CS as a classifier, we use CS as an N-best class selector to limit the secondary classifier input into certain classes. In addition, we use a tree search strategy to select most similar(More)
Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an(More)
In this paper, an N-best class selector based on compressive sensing (CS) algorithm and a tree search strategy is introduced and applied for classification applications and its accuracy and complexity are compared with some well-known classifiers. In this approach, classification is done in three steps. At first, the set of most similar training samples for(More)
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