Hot-word free pre-emption of automated assistant response presentation

    公开(公告)号:US12125477B2

    公开(公告)日:2024-10-22

    申请号:US18235726

    申请日:2023-08-18

    Applicant: GOOGLE LLC

    Abstract: The presentation of an automated assistant response may be selectively pre-empted in response to a hot-word free utterance that is received during the presentation and that is determined to be likely directed to the automated assistant. The determination that the utterance is likely directed to the automated assistant may be performed, for example, using an utterance classification operation that is performed on audio data received during presentation of the response, and based upon such a determination, the response may be pre-empted with another response associated with the later-received utterance. In addition, the duration that is used to determine when a session should be terminated at the conclusion of a conversation between a user and an automated assistant may be dynamically controlled based upon when the presentation of a response has completed.

    TEXT INDEPENDENT SPEAKER RECOGNITION

    公开(公告)号:US20230113617A1

    公开(公告)日:2023-04-13

    申请号:US18078476

    申请日:2022-12-09

    Applicant: GOOGLE LLC

    Abstract: Text independent speaker recognition models can be utilized by an automated assistant to verify a particular user spoke a spoken utterance and/or to identify the user who spoke a spoken utterance. Implementations can include automatically updating a speaker embedding for a particular user based on previous utterances by the particular user. Additionally or alternatively, implementations can include verifying a particular user spoke a spoken utterance using output generated by both a text independent speaker recognition model as well as a text dependent speaker recognition model. Furthermore, implementations can additionally or alternatively include prefetching content for several users associated with a spoken utterance prior to determining which user spoke the spoken utterance.

    Text independent speaker recognition

    公开(公告)号:US11527235B2

    公开(公告)日:2022-12-13

    申请号:US17046994

    申请日:2019-12-02

    Applicant: Google LLC

    Abstract: Text independent speaker recognition models can be utilized by an automated assistant to verify a particular user spoke a spoken utterance and/or to identify the user who spoke a spoken utterance. Implementations can include automatically updating a speaker embedding for a particular user based on previous utterances by the particular user. Additionally or alternatively, implementations can include verifying a particular user spoke a spoken utterance using output generated by both a text independent speaker recognition model as well as a text dependent speaker recognition model. Furthermore, implementations can additionally or alternatively include prefetching content for several users associated with a spoken utterance prior to determining which user spoke the spoken utterance.

    ADAPTIVE INTERFACE IN A VOICE-BASED NETWORKED SYSTEM

    公开(公告)号:US20190318729A1

    公开(公告)日:2019-10-17

    申请号:US15973461

    申请日:2018-05-07

    Applicant: Google LLC

    Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. The system can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. The system can determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. The system can perform speech recognition in each of multiple languages assigned to the user profile, and utilize criteria to select only one of the speech recognitions as appropriate for generating and providing content that is responsive to the spoken utterance.

    Assessing speaker recognition performance

    公开(公告)号:US12154574B2

    公开(公告)日:2024-11-26

    申请号:US18506105

    申请日:2023-11-09

    Applicant: Google LLC

    Abstract: A method for evaluating a verification model includes receiving a first and a second set of verification results where each verification result indicates whether a primary model or an alternative model verifies an identity of a user as a registered user. The method further includes identifying each verification result in the first and second sets that includes a performance metric. The method also includes determining a first score of the primary model based on a number of the verification results identified in the first set that includes the performance metric and determining a second score of the alternative model based on a number of the verification results identified in the second set that includes the performance metric. The method further includes determining whether a verification capability of the alternative model is better than a verification capability of the primary model based on the first score and the second score.

    Assessing speaker recognition performance

    公开(公告)号:US11837238B2

    公开(公告)日:2023-12-05

    申请号:US17076743

    申请日:2020-10-21

    Applicant: Google LLC

    Abstract: A method for evaluating a verification model includes receiving a first and a second set of verification results where each verification result indicates whether a primary model or an alternative model verifies an identity of a user as a registered user. The method further includes identifying each verification result in the first and second sets that includes a performance metric. The method also includes determining a first score of the primary model based on a number of the verification results identified in the first set that includes the performance metric and determining a second score of the alternative model based on a number of the verification results identified in the second set that includes the performance metric. The method further includes determining whether a verification capability of the alternative model is better than a verification capability of the primary model based on the first score and the second score.

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