Low latency real-time vocal tract length normalization
First Claim
1. A method comprising:
- separating training data into speaker specific segments;
performing, for a speaker specific segment of the speaker specific segments;
generating spectral data representative of the speaker specific segment, the spectral data comprising a plurality of warping factors;
selecting a first warping factor as a best warping factor from the plurality of warping factors based on a determination made during speech recognition of the speaker specific segment, and generating a warped spectral data representation of the spectral data using the first warping factor;
comparing the warped spectral data representation to a vocal tract length normalized acoustic model;
iteratively carrying out, until a comparison indicates a warping factor difference below 0.02, the acts of;
selecting an other warping factor and generating an other warped spectral data representation;
comparing the other warped spectral data representation to the vocal tract length normalized acoustic model, to yield the comparison; and
when the other warping factor produces a closer match to the vocal tract length normalized acoustic model, saving the other warping factor as a best warping factor for the speaker specific segment;
training a new acoustic model using a warped spectral data representation of all the training data that is generated using the best warping factor for each of the speaker specific segments;
selecting the new acoustic model as the vocal tract length normalized acoustic model; and
repeating the steps of performing and selecting until the best warping factor for each of the speaker specific segments is stable.
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Abstract
A method and system for training an automatic speech recognition system are provided. The method includes separating training data into speaker specific segments, and for each speaker specific segment, performing the following acts: generating spectral data, selecting a first warping factor and warping the spectral data, and comparing the warped spectral data with a speech model. The method also includes iteratively performing the steps of selecting another warping factor and generating another warped spectral data, comparing the other warped spectral data with the speech model, and if the other warping factor produces a closer match to the speech model, saving the other warping factor as the best warping factor for the speaker specific segment. The system includes modules configured to control a processor in the system to perform the steps of the method.
45 Citations
18 Claims
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1. A method comprising:
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separating training data into speaker specific segments; performing, for a speaker specific segment of the speaker specific segments; generating spectral data representative of the speaker specific segment, the spectral data comprising a plurality of warping factors; selecting a first warping factor as a best warping factor from the plurality of warping factors based on a determination made during speech recognition of the speaker specific segment, and generating a warped spectral data representation of the spectral data using the first warping factor; comparing the warped spectral data representation to a vocal tract length normalized acoustic model; iteratively carrying out, until a comparison indicates a warping factor difference below 0.02, the acts of; selecting an other warping factor and generating an other warped spectral data representation; comparing the other warped spectral data representation to the vocal tract length normalized acoustic model, to yield the comparison; and when the other warping factor produces a closer match to the vocal tract length normalized acoustic model, saving the other warping factor as a best warping factor for the speaker specific segment; training a new acoustic model using a warped spectral data representation of all the training data that is generated using the best warping factor for each of the speaker specific segments; selecting the new acoustic model as the vocal tract length normalized acoustic model; and repeating the steps of performing and selecting until the best warping factor for each of the speaker specific segments is stable. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
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a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising; separating training data into speaker specific segments; performing, for a speaker specific segment of the speaker specific segments; generating spectral data representative of the speaker specific segment, the spectral data comprising a plurality of warping factors; selecting a first warping factor as a best warping factor from the plurality of warping factors based on a determination made during speech recognition of the speaker specific segment, and generating a warped spectral data representation of the spectral data using the first warping factor; comparing the warped spectral data representation to a vocal tract length normalized acoustic model; iteratively carrying out, until a comparison indicates a warping factor difference below 0.02, the acts of; selecting an other warping factor and generating an other warped spectral data representation; comparing the other warped spectral data representation to the vocal tract length normalized acoustic model, to yield the comparison; and when the other warping factor produces a closer match to the vocal tract length normalized acoustic model, saving the other warping factor as a best warping factor for the speaker specific segment; and training a new acoustic model using a warped spectral data representation of all the training data that is generated using the best warping factor for each of the speaker specific segments;
selecting the new acoustic model as the vocal tract length normalized acoustic model; andrepeating the steps of performing and selecting until the best warping factor for each of the speaker specific segments is stable. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:
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separating training data into speaker specific segments; performing, for a speaker specific segment of the speaker specific segments; generating spectral data representative of the speaker specific segment, the spectral data comprising a plurality of warping factors; selecting a first warping factor as a best warping factor from the plurality of warping factors based on a determination made during speech recognition of the speaker specific segment, and generating a warped spectral data representation of the spectral data using the first warping factor; comparing the warped spectral data representation to a vocal tract length normalized acoustic model; iteratively carrying out, until a comparison indicates a warping factor difference below 0.02, the acts of; selecting an other warping factor and generating an other warped spectral data representation; comparing the other warped spectral data representation to the vocal tract length normalized acoustic model, to yield the comparison; and when the other warping factor produces a closer match to the vocal tract length normalized acoustic model, saving the other warping factor as a best warping factor for the speaker specific segment; and training a new acoustic model using a warped spectral data representation of all the training data that is generated using the best warping factor for each of the speaker specific segments;
selecting the new acoustic model as the vocal tract length normalized acoustic model; andrepeating the steps of performing and selecting until the best warping factor for each of the speaker specific segments is stable.
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Specification