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Using a discretized, higher order representation of hidden dynamic variables for speech recognition

  • US 7,680,663 B2
  • Filed: 08/21/2006
  • Issued: 03/16/2010
  • Est. Priority Date: 08/21/2006
  • Status: Active Grant
First Claim
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1. A method of recognizing speech, comprising:

  • training parameters of a generative model based on speech training data indicative of indexed articulatory dynamic values calculated from the speech in the training data having different types of articulatory dynamics, the articulatory dynamic values being of at least second order and being represented by a distribution and the parameters of the generative model including a precision parameter trained based on a precision of the distribution of the articulatory dynamic;

    receiving an observable acoustic value that describes a portion of a speech signal for a current time period under consideration;

    identifying a predicted acoustic value for a hypothesized phonological unit, using the generative model, based on the indexed articulatory dynamics values and depending on indexed articulatory dynamics values calculated for at least two previous time periods; and

    comparing the observed value to the predicted value to determine a likelihood of the hypothesized phonological unit.

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