Grammar confusability metric for speech recognition
First Claim
1. A computer-implemented system that facilitates speech recognition, comprising:
- a vector component for generating feature vectors that approximate acoustical properties of an input term; and
an aggregation component for generating an overall confusability metric based on iterative processing of the term.
2 Assignments
0 Petitions
Accused Products
Abstract
Architecture for testing an application grammar for the presence of confusable terms. A grammar confusability metric (GCM) is generated for describing a likelihood that a reference term will be confused by the speech recognizer with another term phrase currently allowed by active grammar rules. The GCM is used to flag processing of two phrases in the grammar that have different semantic meaning, but that the speech recognizer could have difficulty distinguishing reliably. A built-in acoustic model is analyzed and feature vectors generated that are close to the acoustic properties of the input term. The feature vectors are then sent for recognition. A statistically random sampling method is applied to explore the acoustic properties of feature vectors of the input term phrase spatially and temporally. The feature vectors are perturbed in the neighborhood of the time domain and the Gaussian mixture model to which the feature vectors belong.
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Citations
20 Claims
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1. A computer-implemented system that facilitates speech recognition, comprising:
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a vector component for generating feature vectors that approximate acoustical properties of an input term; and an aggregation component for generating an overall confusability metric based on iterative processing of the term. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method of performing speech recognition, comprising:
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converting an input term into a set of senone IDs; randomly selecting feature vectors that are representative of distributions of the set of senone IDs; driving a recognition process using the feature vectors to output a result; and aggregating results from multiple iterations of the input term into an overall confusability metric. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer-implemented system, comprising:
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computer-implemented means for converting an input term into a set of senone IDs; computer-implemented means for randomly selecting feature vectors that are representative of distributions of the set of senone IDs; computer-implemented means for driving a recognition process using the feature vectors to output a result; and computer-implemented means for aggregating results from multiple iterations of the input term into an overall confusability metric.
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Specification