Error corrective mechanisms for consensus decoding of speech
AssigneeInternational Business Machines Corporation
InventorsLidia Luminita Mangu, Mukund Padmanabhan
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AssigneeInternational Business Machines Corporation
InventorsLidia Luminita Mangu, Mukund Padmanabhan
Estimating the term…
Techniques are described for decreasing the number of errors when consensus decoding is used during speech recognition. A number of corrective rules are applied to confusion sets that are extracted during real-time speech recognition. The corrective rules are determined during training of the speech recognition system, which entails using many training confusion sets. A learning process is used that generates a number of possible rules, called template rules, that can be applied to the training confusion sets. The learning process also determines the corrective rules from the template rules. The corrective rules operate on the real-time confusion sets to select hypothesis words from the confusion sets, where the hypothesis words are not necessarily the words having the highest score.
A method for correcting errors for consensus decoding of speech, the method comprising the steps of:
A method for determining a plurality of corrective rules from a plurality of training confusion sets, the method comprising the steps of:
A system for correcting errors for consensus decoding of speech, comprising:
A system for determining a plurality of corrective rules from a plurality of training confusion sets, comprising:
An article of manufacture for correcting errors for consensus decoding of speech, comprising:
An article of manufacture for determining a plurality of corrective rules from a plurality of training confusion sets, comprising:
A system for correcting errors for consensus decoding of speech, comprising:
A system for determining a plurality of corrective rules from a plurality of training confusion sets, comprising:
Dependencies are inferred from the claim text.