Discriminative clustering methods for automatic speech recognition
First Claim
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1. A discriminative clustering method for automatic speech recognition, comprising:
- providing a set of Gaussian distributions corresponding an acoustic vector space;
testing said distributions to identify selected distributions having a predetermined proximity to one another based on a distance measure and merging said selected distributions into at least one new distribution;
computing the centroid of said new distribution by minimizing the Bhattacharyya distance between selected distribution and its centroid.
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Abstract
The discriminative clustering technique tests a provided set of Gaussian distributions corresponding to an acoustic vector space. A distance metric, such as the Bhattacharyya distance, is used to assess which distributions are sufficiently proximal to be merged into a new distribution. Merging is accomplished by computing the centroid of the new distribution by minimizing the Bhattacharyya distance between the parameters of the Gaussian distributions being merged.
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Citations
13 Claims
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1. A discriminative clustering method for automatic speech recognition, comprising:
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providing a set of Gaussian distributions corresponding an acoustic vector space;
testing said distributions to identify selected distributions having a predetermined proximity to one another based on a distance measure and merging said selected distributions into at least one new distribution;
computing the centroid of said new distribution by minimizing the Bhattacharyya distance between selected distribution and its centroid. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for automatic speech recognition, comprising:
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providing a set of Gaussian distributions corresponding an acoustic vector space;
a step for testing said distributions to identify selected distributions;
a step for merging said selected distributions into at least one new distribution; and
a step for computing the centroid of said new distribution. - View Dependent Claims (8, 9)
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10. A discriminative clustering method for automatic speech recognition, comprising:
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providing a set of Gaussian distributions corresponding an acoustic vector space;
computing a Bhattacharyya distance measure between each of said distributions;
selecting distributions having a predetermined proximity to one another based on a distance measure; and
merging said selected distributions into at least one new distribution. - View Dependent Claims (11, 12, 13)
where μ
2 and μ
2 are the mean vectors associated with distributions g1 and g2 and Σ
1 and Σ
2 are the respective covariance matrices.
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12. The method of claim 10 further comprising:
computing a centroid for said new distribution by minimizing the Bhattacharyya distance between said selected distributions.
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13. The method of claim 10 wherein the centroid of the new distribution which contains Gaussian distributions gi with a weight of wi, i=1, . . . , N is computed in accordance with
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c = ∑ i w i * ( σ c 2 + σ i 2 ) - 1 * μ i ∑ i w i * ( σ c 2 + σ i 2 ) - 1 where σ
2 is the variance and μ
is the mean associated with a specific component of the feature vector.
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Specification