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Method and System for Selectively Biased Linear Discriminant Analysis in Automatic Speech Recognition Systems

  • US 20140058731A1
  • Filed: 08/23/2013
  • Published: 02/27/2014
  • Est. Priority Date: 08/24/2012
  • Status: Active Grant
First Claim
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1. A method for training an acoustic model using the maximum likelihood criteria, comprising the steps of:

  • a. performing a forced alignment of speech training data;

    b. processing the training data and obtaining estimated scatter matrices, wherein said scatter matrices may comprise one or more of a between class scatter matrix and a within-class scatter matrix, from which mean vectors may be estimated;

    c. biasing the between class scatter matrix and the within-class scatter matrix;

    d. diagonalizing the between class scatter matrix and the within class scatter matrix and estimating eigen-vectors to produce transformed scatter matrices;

    e. obtaining new discriminative features using the estimated vectors, wherein said vectors correspond to the highest discrimination in the new space;

    f. training a new acoustic model based on said new discriminative features; and

    g. saving said acoustic model.

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