Speaker adaptation of neural network acoustic models using I-vectors
First Claim
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1. A method comprising:
- providing a deep neural network acoustic model;
receiving audio data including one or more utterances of a speaker;
extracting a plurality of speech recognition features from the one or more utterances of the speaker;
creating a speaker identity vector for the speaker based on the speech recognition features extracted from the one or more utterances of the speaker;
performing, by a computer system, an automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector by executing the deep neural network acoustic model; and
adapting the deep neural network acoustic model executing on the computer system performing the automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector, wherein adapting the deep neural network acoustic model further comprises concatenating the speaker identity vector to each of the speech recognition features extracted from the one or more utterances of the speakers to form an input to the deep neural network acoustic model.
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Abstract
A method includes providing a deep neural network acoustic model, receiving audio data including one or more utterances of a speaker, extracting a plurality of speech recognition features from the one or more utterances of the speaker, creating a speaker identity vector for the speaker based on the extracted speech recognition features, and adapting the deep neural network acoustic model for automatic speech recognition using the extracted speech recognition features and the speaker identity vector.
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10 Claims
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1. A method comprising:
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providing a deep neural network acoustic model; receiving audio data including one or more utterances of a speaker; extracting a plurality of speech recognition features from the one or more utterances of the speaker; creating a speaker identity vector for the speaker based on the speech recognition features extracted from the one or more utterances of the speaker; performing, by a computer system, an automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector by executing the deep neural network acoustic model; and adapting the deep neural network acoustic model executing on the computer system performing the automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector, wherein adapting the deep neural network acoustic model further comprises concatenating the speaker identity vector to each of the speech recognition features extracted from the one or more utterances of the speakers to form an input to the deep neural network acoustic model. - View Dependent Claims (2, 3, 4, 5)
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6. A computer program product for adapting deep neural network acoustic models for automatic speech recognition, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:
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providing a deep neural network acoustic model; receiving audio data including one or more utterances of a speaker; extracting a plurality of speech recognition features from the one or more utterances of the speaker; creating a speaker identity vector for the speaker based on the speech recognition features extracted from the one or more utterances of the speaker; performing, by the processor, an automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector by executing the deep neural network acoustic model; and adapting the deep neural network acoustic model executing on a computer system the processor performing the automatic speech recognition using the speech recognition features extracted from the one or more utterances of the speaker and the speaker identity vector, wherein adapting the deep neural network acoustic model further comprises concatenating the speaker identity vector to each of the speech recognition features extracted from the one or more utterances of the speakers to form an input to the deep neural network acoustic model. - View Dependent Claims (7, 8, 9, 10)
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Specification