Method and apparatus for training a text independent speaker recognition system using speech data with text labels
First Claim
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1. A Hidden Markov Model (HMM) speaker recognition system, comprising:
- at least one data storage unit; and
at least one processor programmed to;
create a Gaussian Mixture Model (GMM) by pooling Gaussians from a plurality of HMM states; and
normalize Gaussian weights with respect to the plurality of HMM states to provide a Text Independent (TI) speaker recognition mode in the HMM speaker recognition system;
wherein the HMM speaker recognition system is selected from a group consisting of a Text Dependent (TD) HMM speaker recognition system and a Text Constrained (TC) speaker recognition system.
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Abstract
There is provided an apparatus for providing a Text Independent (TI) speaker recognition mode in a Text Dependent (TD) Hidden Markov Model (HMM) speaker recognition system and/or a Text Constrained (TC) HMM speaker recognition system. The apparatus includes a Gaussian Mixture Model (GMM) generator and a Gaussian weight normalizer. The GMM generator is for creating a GMM by pooling Gaussians from a plurality of HMM states. The Gaussian weight normalizer is for normalizing Gaussian weights with respect to the plurality of HMM states.
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Citations
13 Claims
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1. A Hidden Markov Model (HMM) speaker recognition system, comprising:
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at least one data storage unit; and at least one processor programmed to; create a Gaussian Mixture Model (GMM) by pooling Gaussians from a plurality of HMM states; and normalize Gaussian weights with respect to the plurality of HMM states to provide a Text Independent (TI) speaker recognition mode in the HMM speaker recognition system; wherein the HMM speaker recognition system is selected from a group consisting of a Text Dependent (TD) HMM speaker recognition system and a Text Constrained (TC) speaker recognition system. - View Dependent Claims (2, 3, 4)
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5. A Text Independent (TI) Gaussian Mixture Model (GMM) speaker recognition system, comprising:
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at least one data storage unit; and at least one processor programmed to; create a Hidden Markov Model (HMM) by assigning states to Gaussians from a GMM; and calculate state transition probabilities and Gaussian weights with respect to a plurality of HMM states to provide a Text Dependent (TD) HMM speaker recognition mode and/or a Text Constrained (TC) HMM speaker recognition mode in the TI GMM speaker recognition system. - View Dependent Claims (6, 7, 8)
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9. A first Hidden Markov Model (HMM) speaker recognition system, comprising:
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at least one data storage unit; and at least one processor programmed to; create an HMM with a smaller number of states or a larger number of states by pooling Gaussians from a plurality of HMM states into a single HMM state or splitting the Gaussians from the plurality of HMM states into different HMM states, respectively; and normalize Gaussian weights with respect to the HMM states to provide a TD HMM speaker recognition mode and/or a TC HMM speaker recognition mode in a second HMM speaker recognition system; wherein the first HMM speaker recognition system is selected from a group consisting of a Text Dependent (TD) HMM speaker recognition system and a Text Constrained (TC) speaker recognition system. - View Dependent Claims (10, 11, 12, 13)
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Specification