Generating a task-adapted acoustic model from one or more supervised and/or unsupervised corpora
First Claim
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1. An acoustic model (AM) for use in a speech recognition system, the AM comprising:
- parameters indicative of unsupervised data weighted by a speech recognition confidence measure applied at a sub-utterance level.
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Abstract
Unsupervised speech data is provided to a speech recognizer that recognizes the speech data and outputs a recognition result along with a confidence measure for each recognized word. A task-related acoustic model is generated based on the recognition result, the speech data and the confidence measure. The speech data can be weighted by the confidence measure in generating the acoustic model so that only data that has been recognized with a high degree of confidence will weigh heavily in generation of the acoustic model.
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Citations
33 Claims
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1. An acoustic model (AM) for use in a speech recognition system, the AM comprising:
parameters indicative of unsupervised data weighted by a speech recognition confidence measure applied at a sub-utterance level. - View Dependent Claims (2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17)
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8. A method of generating an acoustic model (AM) for use in a speech recognition system, corresponding:
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receiving unsupervised speech data as utterances formed of sub-utterances units, the utterances being represented by acoustic data;
generating a transcription for each utterance in the unsupervised speech data and a confidence measure for each sub-utterance unit, with a speech recognizer; and
generating a confidence measure weighted AM based on the acoustic data and the transcriptions weighted by the confidence measures on a sub-utterance level. - View Dependent Claims (9, 18, 19)
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20. A method of generating an acoustic model (AM) for a speech recognition system, comprising:
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receiving a task-dependent (TD) AM generated from task-dependent speech data, relevant to a desired task to be performed by the speech recognition system;
receiving a task-independent (TI) AM generated from task-independent speech data, the TI AM and the TD AM each including Gaussian means and variances; and
combining the Gaussian means and variances based on an amount of data used to generate each mean and each variance to obtain a composite AM. - View Dependent Claims (21, 22, 23)
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24. An acoustic model (AM) generation system, comprising:
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a speech recognizer receiving unsupervised speech data in the form of utterances with sub-utterance units and generating a transcription of the utterances and a confidence measure associated with each sub-utterance unit; and
an AM generator receiving the transcription and confidence measures and generating a confidence measure AM by weighting each word with its confidence measure. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33)
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25. The AM generation system of cliam 24 wherein the AM generator comprises:
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a weighting component weighting each word with its associated confidence measure; and
an AM training component training the confidence measure AM based on the weighted words.
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Specification