Dynamic N-best algorithm to reduce speech recognition errors
First Claim
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1. A method for reducing speech recognition errors, the method comprising:
- receiving an N-best list associated with a user utterance, the N-best list including one or more hypotheses and associated confidence values;
classifying the user utterance in response to the N-best list resulting in a classification;
selecting a re-scoring algorithm that is tuned for the classification;
applying the re-scoring algorithm to the N-best list to create a re-scored N-best list; and
selecting a hypothesis for the value of the user utterance based on the re-scored N-best list.
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Abstract
A method for reducing speech recognition errors. The method includes receiving an N-best list associated with a user utterance. The N-best list includes one or more hypotheses and associated confidence values. The user utterance is classified in response to the N-best list, resulting in a classification. A re-scoring algorithm that is tuned for the classification is selected. The re-scoring algorithm is applied to the N-best list to create a re-scored N-best list. A hypothesis for the value of the user utterance is selected based on the re-scored N-best list.
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Citations
19 Claims
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1. A method for reducing speech recognition errors, the method comprising:
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receiving an N-best list associated with a user utterance, the N-best list including one or more hypotheses and associated confidence values;
classifying the user utterance in response to the N-best list resulting in a classification;
selecting a re-scoring algorithm that is tuned for the classification;
applying the re-scoring algorithm to the N-best list to create a re-scored N-best list; and
selecting a hypothesis for the value of the user utterance based on the re-scored N-best list. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer program product for providing a dynamic N-best algorithm to reduce speech recognition errors, the computer program product comprising:
a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising;
receiving an N-best list associated with a user utterance, the N-best list including one or more hypotheses and associated confidence values;
classifying the user utterance in response to the N-best list resulting in a classification;
selecting a re-scoring algorithm that is tuned for the classification;
applying the re-scoring algorithm to the N-best list to create a re-scored N-best list; and
selecting a hypothesis for the value of the user utterance based on the re-scored N-best list.
Specification