Speech recognition hypothesis generation according to previous occurrences of hypotheses terms and/or contextual data

    公开(公告)号:US11189264B2

    公开(公告)日:2021-11-30

    申请号:US16614241

    申请日:2019-07-17

    Applicant: Google LLC

    Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.

    SPEECH RECOGNITION HYPOTHESIS GENERATION ACCORDING TO PREVIOUS OCCURRENCES OF HYPOTHESES TERMS AND/OR CONTEXTUAL DATA

    公开(公告)号:US20220084503A1

    公开(公告)日:2022-03-17

    申请号:US17536938

    申请日:2021-11-29

    Applicant: GOOGLE LLC

    Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.

    SPEECH RECOGNITION HYPOTHESIS GENERATION ACCORDING TO PREVIOUS OCCURRENCES OF HYPOTHESES TERMS AND/OR CONTEXTUAL DATA

    公开(公告)号:US20210012765A1

    公开(公告)日:2021-01-14

    申请号:US16614241

    申请日:2019-07-17

    Applicant: Google LLC

    Abstract: Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.

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