Cascade processing for speeding up sliding window sparse classification

Sparse representations have been found to provide high classification accuracy in many fields. Their drawback is the high computational load. In this work, we propose a novel cascaded classifier structure to speed up the decision process while utilizing sparse signal representation. In particular, we apply the cascaded decision process for noise robust automatic speech recognition task. The cascaded decision process is implemented using a feedforward neural network (NN) and time sparse versions of a non-negative matrix factorization (NMF) based sparse classification method of [1]. The recognition accuracy of our cascade is among the three best in the recent CHiME2013 benchmark and obtains six times faster the accuracy of NMF alone as in [1].

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