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Sparse representation features for speech recognition

  • US 8,484,023 B2
  • Filed: 09/24/2010
  • Issued: 07/09/2013
  • Est. Priority Date: 09/24/2010
  • Status: Active Grant
First Claim
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1. A method, comprising:

  • obtaining a test vector and a training data set associated with a speech recognition system;

    selecting a subset of the training data set;

    mapping the test vector with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint; and

    training, using a processor, an acoustic model on the new test feature set.

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