Sparse representation features for speech recognition
First Claim
1. A method, comprising:
- obtaining a test vector and a training data set associated with a speech recognition system;
selecting a subset of the training data set;
mapping the test vector with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint; and
training, using a processor, an acoustic model on the new test feature set.
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Abstract
Techniques are disclosed for generating and using sparse representation features to improve speech recognition performance. In particular, principles of the invention provide sparse representation exemplar-based recognition techniques. For example, a method comprises the following steps. A test vector and a training data set associated with a speech recognition system are obtained. A subset of the training data set is selected. The test vector is mapped with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint. An acoustic model is trained on the new test feature set. The acoustic model trained on the new test feature set may be used to decode user speech input to the speech recognition system.
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Citations
25 Claims
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1. A method, comprising:
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obtaining a test vector and a training data set associated with a speech recognition system; selecting a subset of the training data set; mapping the test vector with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint; and training, using a processor, an acoustic model on the new test feature set. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. An apparatus, comprising:
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a memory; and a processor operatively coupled to the memory and configured to; obtain a test vector and a training data set associated with a speech recognition system; select a subset of the training data set; map the test vector with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint; and train an acoustic model on the new test feature set. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A non-transitory computer readable storage medium having tangibly embodied thereon computer readable program code which, when executed, causes a processor device to:
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obtain a test vector and a training data set associated with a speech recognition system; select a subset of the training data set; map the test vector with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint; and train an acoustic model on the new test feature set.
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Specification