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Discriminative training for speaker and speech verification

  • US 7,454,339 B2
  • Filed: 12/20/2005
  • Issued: 11/18/2008
  • Est. Priority Date: 12/20/2005
  • Status: Expired due to Fees
First Claim
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1. A method for discriminatively training acoustic models for automated speech verification, comprising:

  • defining a likelihood ratio for a given speech segment X having a known linguist identity W, using an acoustic model which represents W and an alternative acoustic model which represents linguist identities other than W;

    determining an average likelihood ratio score for the likelihood ratio scores over a set of training utterances whose linguist identities are the same, W;

    determining an average likelihood ratio score for the likelihood ratio scores over a competing set of training utterances whose linguist identities are not W; and

    optimizing a difference between the average likelihood ratio score over the set of training utterances and the average likelihood ratio score over the competing set of training utterances, thereby improving the acoustic model.

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