Language model biasing modulation
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).
1. A computer-implemented method comprising:
receiving, by an automated speech recognizer (ASR) that is configured to use a language model that has previously been biased for use in transcribing utterances, audio data corresponding to an utterance;
determining that, when the audio data was received, a particular context that is associated with biasing the language model was still applicable;
in response to determining that the particular context that is associated with biasing the language mode was still applicable when the audio data was received, generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances; and
providing a representation of the transcription for output.
2. The method of claim 1 , wherein generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances comprises:
not returning the biased language model to a baseline state, or reducing an effect of the previous biasing of the language model.
3. The method of claim 1 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a predetermined period of time has not elapsed.
4. The method of claim 1 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely in a same context.
5. The method of claim 1 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a confidence score associated with the particular context does satisfy a threshold.
6. The method of claim 1 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely associated with another context that is consistent with the particular context.
7. The method of claim 1 , wherein determining that, when the audio data was received, a particular context that is associated with biasing the language model was still applicable comprises:
determining that interactions from a user indicate a change in behavior since the language model was biased where the change is within a threshold amount.
8. A computer storage medium storing instructions that, when executed by data processing apparatus, cause one or more computers to perform operations comprising:
receiving, by an automated speech recognizer (ASR) that is configured to use a language model that has previously been biased for use in transcribing utterances, audio data corresponding to an utterance;
determining that, when the audio data was received, a particular context that is associated with biasing the language model was still applicable;
in response to determining that the particular context that is associated with biasing the language mode was still applicable when the audio data was received, generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances; and
providing a representation of the transcription for output.
9. The medium of claim 8 , wherein generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances comprises:
not returning the biased language model to a baseline state, or reducing an effect of the previous biasing of the language model.
10. The medium of claim 8 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a predetermined period of time has not elapsed.
11. The medium of claim 8 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely in a same context.
12. The medium of claim 8 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a confidence score associated with the particular context does satisfy a threshold.
13. The medium of claim 8 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely associated with another context that is consistent with the particular context.
14. The medium of claim 8 , wherein determining that, when the audio data was received, a particular context that is associated with biasing the language model was still applicable comprises:
determining that interactions from a user indicate a change in behavior since the language model was biased where the change is within a threshold amount.
15. A system comprising:
one or more computers including one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising:
receiving, by an automated speech recognizer (ASR) that is configured to use a language model that has previously been biased for use in transcribing utterances, audio data corresponding to an utterance;
determining that, when the audio data was received, a particular context that is associated with biasing the language model was still applicable;
in response to determining that the particular context that is associated with biasing the language mode was still applicable when the audio data was received,
generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances; and
providing a representation of the transcription for output.
16. The system of claim 15 , wherein generating, by the ASR, a transcription of the utterance using the language model that has previously been biased for use in transcribing utterances comprises:
not returning the biased language model to a baseline state, or reducing an effect of the previous biasing of the language model.
17. The system of claim 15 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a predetermined period of time has not elapsed.
18. The system of claim 15 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely in a same context.
19. The system of claim 15 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a confidence score associated with the particular context does satisfy a threshold.
20. The system of claim 15 , wherein determining that the particular context that is associated with biasing the language model was still applicable comprises determining that a user that spoke the utterance is likely associated with another context that is consistent with the particular context.