Training of homoscedastic hidden Markov models for automatic speech recognition
First Claim
1. A method for training a speech recognizer in a speech recognition system, said method comprising the steps of:
- providing a data base containing a plurality of acoustic speech units;
generating a homoscedastic hidden Markov model (HMM) from said plurality of acoustic speech units in said data base;
said generating step comprises forming a set of pooled training data from said plurality of acoustic speech units and estimating a single global covariance matrix using said pooled training data set, said single global covariance matrix representing a tied covariance matrix for every Gaussian probability density function (PDF) for every state of every hidden Markov model structure in said homoscedastic hidden Markov model; and
loading said homoscedastic hidden Markov model into the speech recognizer.
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Abstract
A method for training a speech recognizer in a speech recognition system is described. The method of the present invention comprises the steps of providing a data base containing acoustic speech units, generating a homoscedastic hidden Markov model from the acoustic speech units in the data base, and loading the homoscedastic hidden Markov model into the speech recognizer. The hidden Markov model loaded into the speech recognizer has a single covariance matrix which represents the tied covariance matrix of every Gaussian probability density function PDF for every state of every hidden Markov model structure in the homoscedastic hidden Markov model.
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10 Claims
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1. A method for training a speech recognizer in a speech recognition system, said method comprising the steps of:
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providing a data base containing a plurality of acoustic speech units; generating a homoscedastic hidden Markov model (HMM) from said plurality of acoustic speech units in said data base; said generating step comprises forming a set of pooled training data from said plurality of acoustic speech units and estimating a single global covariance matrix using said pooled training data set, said single global covariance matrix representing a tied covariance matrix for every Gaussian probability density function (PDF) for every state of every hidden Markov model structure in said homoscedastic hidden Markov model; and loading said homoscedastic hidden Markov model into the speech recognizer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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