Dynamic N-best algorithm to reduce recognition errors
First Claim
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1. A method for reducing recognition errors, the method comprising:
- receiving an N-best list associated with an input of a computer based recognition system, the N-best list including one or more hypotheses and associated confidence values;
classifying the input in response to the N-best list to result in a classification into a bin from a plurality of bins according to characteristics of the N-best list;
selecting a re-scoring algorithm from a plurality of rescoring algorithms that are tuned for the classification, wherein the re-scoring algorithm is selected on a per bin basis;
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 input based on the re-scored N-best list.
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Abstract
A method for reducing recognition errors. The method includes receiving an N-best list associated with an input of a computer based recognition system. The N-best list includes one or more hypotheses and associated confidence values. The input 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 input is selected based on the re-scored N-best list.
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Citations
22 Claims
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1. A method for reducing recognition errors, the method comprising:
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receiving an N-best list associated with an input of a computer based recognition system, the N-best list including one or more hypotheses and associated confidence values; classifying the input in response to the N-best list to result in a classification into a bin from a plurality of bins according to characteristics of the N-best list; selecting a re-scoring algorithm from a plurality of rescoring algorithms that are tuned for the classification, wherein the re-scoring algorithm is selected on a per bin basis; 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 input 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, 19, 20)
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21. A computer implemented method for providing a dynamic N-best algorithm to reduce recognition errors, the method comprising:
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receiving an N-best list associated with an input of a computer based recognition system, the N-best list including one or more hypotheses and associated confidence values; classifying the input in response to the N-best list to result in a classification into a bin from a plurality of bins according to characteristics of the N-best list; selecting a re-scoring algorithm from a plurality of rescoring algorithms that are tuned for the classification, wherein the re-scoring algorithm is selected on a per bin basis; 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 input based on the re-scored N-best list.
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22. A system for reducing recognition errors, the system comprising a host system in communication with a computer based recognition system, the host system including instructions to implement a method comprising:
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receiving an N-best list associated with an input to the computer based recognition system, the N-best list including one or more hypotheses and associated confidence values; classifying the input in response to the N-best list to result in a classification into a bin from a plurality of bins according to characteristics of the N-best list; selecting a re-scoring algorithm from a plurality of rescoring algorithms that are tuned for the classification, wherein the re-scoring algorithm is selected on a per bin basis; 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 input based on the re-scored N-best list.
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Specification