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Deep belief network for large vocabulary continuous speech recognition

  • US 8,972,253 B2
  • Filed: 09/15/2010
  • Issued: 03/03/2015
  • Est. Priority Date: 09/15/2010
  • Status: Active Grant
First Claim
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1. A method executed by a processor, the method comprising:

  • receiving a sample at a context-dependent combination of a Deep Belief Network (DBN) and a Hidden Markov Model (HMM), wherein the sample is a spoken utteranceoutputting, at the DBN, a posterior probability distribution over labeled senones;

    outputting, at the HMM, transition probabilities between the labeled senones, the transition probabilities based upon the posterior probability distribution over the labeled senones; and

    decoding the sample based at least in part upon the posterior probability distribution over the labeled senones and the transition probabilities between the labeled senones.

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