REVISING LANGUAGE MODEL SCORES BASED ON SEMANTIC CLASS HYPOTHESES

摘要

Techniques for improved speech recognition disclosed herein include applying a statistical language model to a free-text input utterance to obtain a plurality of candidate word sequences for automatic speech recognition of the input utterance, each of the plurality of candidate word sequences having a corresponding initial score generated by the statistical language model. For one or more of the plurality of candidate word sequences, each of the one or more candidate word sequences may be analyzed to generate one or more hypotheses for a semantic class of at least one token in the respective candidate word sequence. The initial scores generated by the statistical language model for at least the one or more candidate word sequences may be revised based at least in part on the one or more hypotheses for the semantic class of the at least one token in each of the one or more candidate word sequences.

主权项

1. A method comprising:
applying a statistical language model to a free-text input utterance to obtain a plurality of candidate word sequences for automatic speech recognition of the input utterance, each of the plurality of candidate word sequences having a corresponding initial score generated by the statistical language model; for one or more of the plurality of candidate word sequences, analyzing each of the one or more candidate word sequences, using at least one processor, to generate one or more hypotheses for a semantic class of at least one token in the respective candidate word sequence; and revising the initial scores for at least the one or more candidate word sequences based at least in part on the one or more hypotheses for the semantic class of the at least one token in each of the one or more candidate word sequences.