Abstract: Systems and methods for processing a query include determining a plurality of sets of match candidates for a query using a processor, each of the plurality of sets of match candidates being independently determined from a plurality of diverse word lattice generation components of different type. The plurality of sets of match candidates is merged by generating a first score for each match candidate to provide a merged set of match candidates. A second score is computed for each match candidate of the merged set based upon features of that match candidate. The first score and the second score are combined to provide a final set of match candidates as matches to the query.

Abstract: Systems and methods for processing a query include determining a plurality of sets of match candidates for a query using a processor, each of the plurality of sets of match candidates being independently determined from a plurality of diverse word lattice generation components of different type. The plurality of sets of match candidates is merged by generating a first score for each match candidate to provide a merged set of match candidates. A second score is computed for each match candidate of the merged set based upon features of that match candidate. The first score and the second score are combined to provide a final set of match candidates as matches to the query.

Abstract: 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.

Abstract: 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.