EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS

    公开(公告)号:US20240135934A1

    公开(公告)日:2024-04-25

    申请号:US18483492

    申请日:2023-10-09

    Applicant: Google LLC

    CPC classification number: G10L17/06 G10L17/02 G10L17/04

    Abstract: A method includes obtaining a multi-utterance training sample that includes audio data characterizing utterances spoken by two or more different speakers and obtaining ground-truth speaker change intervals indicating time intervals in the audio data where speaker changes among the two or more different speakers occur. The method also includes processing the audio data to generate a sequence of predicted speaker change tokens using a sequence transduction model. For each corresponding predicted speaker change token, the method includes labeling the corresponding predicted speaker change token as correct when the predicted speaker change token overlaps with one of the ground-truth speaker change intervals. The method also includes determining a precision metric of the sequence transduction model based on a number of the predicted speaker change tokens labeled as correct and a total number of the predicted speaker change tokens in the sequence of predicted speaker change tokens.

    ATTENTIVE SCORING FUNCTION FOR SPEAKER IDENTIFICATION

    公开(公告)号:US20240029742A1

    公开(公告)日:2024-01-25

    申请号:US18479615

    申请日:2023-10-02

    Applicant: Google LLC

    CPC classification number: G10L17/06 G06F16/245 G06N3/08 G10L17/04 G10L17/18

    Abstract: A speaker verification method includes receiving audio data corresponding to an utterance, processing the audio data to generate a reference attentive d-vector representing voice characteristics of the utterance, the evaluation ad-vector includes ne style classes each including a respective value vector concatenated with a corresponding routing vector. The method also includes generating using a self-attention mechanism, at least one multi-condition attention score that indicates a likelihood that the evaluation ad-vector matches a respective reference ad-vector associated with a respective user. The method also includes identifying the speaker of the utterance as the respective user associated with the respective reference ad-vector based on the multi-condition attention score.

    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.

    Assessing Speaker Recognition Performance
    7.
    发明公开

    公开(公告)号:US20240079013A1

    公开(公告)日:2024-03-07

    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.

    Attentive scoring function for speaker identification

    公开(公告)号:US11798562B2

    公开(公告)日:2023-10-24

    申请号:US17302926

    申请日:2021-05-16

    Applicant: Google LLC

    CPC classification number: G10L17/06 G06F16/245 G06N3/08 G10L17/04 G10L17/18

    Abstract: A speaker verification method includes receiving audio data corresponding to an utterance, processing the audio data to generate a reference attentive d-vector representing voice characteristics of the utterance, the evaluation ad-vector includes ne style classes each including a respective value vector concatenated with a corresponding routing vector. The method also includes generating using a self-attention mechanism, at least one multi-condition attention score that indicates a likelihood that the evaluation ad-vector matches a respective reference ad-vector associated with a respective user. The method also includes identifying the speaker of the utterance as the respective user associated with the respective reference ad-vector based on the multi-condition attention score.

    Hybrid Multilingual Text-Dependent and Text-Independent Speaker Verification

    公开(公告)号:US20220310098A1

    公开(公告)日:2022-09-29

    申请号:US17211791

    申请日:2021-03-24

    Applicant: Google LLC

    Abstract: A speaker verification method includes receiving audio data corresponding to an utterance, processing a first portion of the audio data that characterizes a predetermined hotword to generate a text-dependent evaluation vector, and generating one or more text-dependent confidence scores. When one of the text-dependent confidence scores satisfies a threshold, the operations include identifying a speaker of the utterance as a respective enrolled user associated with the text-dependent confidence score that satisfies the threshold and initiating performance of an action without performing speaker verification. When none of the text-dependent confidence scores satisfy the threshold, the operations include processing a second portion of the audio data that characterizes a query to generate a text-independent evaluation vector, generating one or more text-independent confidence scores, and determining whether the identity of the speaker of the utterance includes any of the enrolled users.

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