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EFFICIENT GENERATION OF COMPLEMENTARY ACOUSTIC MODELS FOR PERFORMING AUTOMATIC SPEECH RECOGNITION SYSTEM COMBINATION

  • US 20160034811A1
  • Filed: 09/30/2014
  • Published: 02/04/2016
  • Est. Priority Date: 07/31/2014
  • Status: Abandoned Application
First Claim
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1. A method for generating complementary acoustic models for performing automatic speech recognition system combination, the method comprising:

  • at a device with a processor and memory storing instructions for execution by the processor;

    training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, wherein training the deep neural network comprises;

    determining, using the set of training data, a set of optimal weighting values of the deep neural network; and

    storing the set of optimal weighting values in the memory;

    linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

    training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein training the Gaussian-mixture model comprises;

    determining, using the set of training data, a set of optimal parameter values of the Gaussian-mixture model; and

    storing the set of optimal parameter values in the memory.

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