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    Speech based user recognition
    15.
    发明授权
    Speech based user recognition 有权

    公开(公告)号：US11270685B2

    公开(公告)日：2022-03-08

    申请号：US16726051

    申请日：2019-12-23

    Applicant: Amazon Technologies, Inc.

    Inventor： Spyridon Matsoukas ,  Aparna Khare ,  Vishwanathan Krishnamoorthy ,  Shamitha Somashekar ,  Arindam Mandal

    IPC: G10L15/01 ,  G10L15/25 ,  G10L15/30

    Abstract: Systems, methods, and devices for verifying a user are disclosed. A speech-controlled device captures a spoken command, and sends audio data corresponding thereto to a server. The server performs ASR on the audio data to determine ASR confidence data. The server, in parallel, performs user verification on the audio data to determine user verification confidence data. The server may modify the user verification confidence data using the ASR confidence data. In addition or alternatively, the server may modify the user verification confidence data using at least one of a location of the speech-controlled device within a building, a type of the speech-controlled device, or a geographic location of the speech-controlled device.

    Voice profile updating
    17.
    发明授权
    Voice profile updating 有权

    公开(公告)号：US11004454B1

    公开(公告)日：2021-05-11

    申请号：US16182021

    申请日：2018-11-06

    Applicant: Amazon Technologies, Inc.

    Inventor： Sundararajan Srinivasan ,  Arindam Mandal ,  Krishna Subramanian ,  Spyridon Matsoukas ,  Aparna Khare ,  Rohit Prasad

    IPC: G10L17/04 ,  G06F3/16 ,  G10L17/00 ,  G10L15/06

    Abstract: Techniques for updating voice profiles used to perform user recognition are described. A system may use clustering techniques to update voice profiles. When the system receives audio data representing a spoken user input, the system may store the audio data. Periodically, the system may recall, from storage, audio data (representing previous user inputs). The system may identify clusters of the audio data, with each cluster including similar or identical speech characteristics. The system may determine a cluster is substantially similar to an existing voice profile. If this occurs, the system may create an updated voice profile using the original voice profile and the cluster of audio data.

    Speech based user recognition
    18.
    发明授权
    Speech based user recognition 有权

    公开(公告)号：US10522134B1

    公开(公告)日：2019-12-31

    申请号：US15388458

    申请日：2016-12-22

    Applicant: Amazon Technologies, Inc.

    Inventor： Spyridon Matsoukas ,  Aparna Khare ,  Vishwanathan Krishnamoorthy ,  Shamitha Somashekar ,  Arindam Mandal

    IPC: G10L15/01 ,  G10L15/25 ,  G10L15/30

    Abstract: Systems, methods, and devices for verifying a user are disclosed. A speech-controlled device captures a spoken command, and sends audio data corresponding thereto to a server. The server performs ASR on the audio data to determine ASR confidence data. The server, in parallel, performs user verification on the audio data to determine user verification confidence data. The server may modify the user verification confidence data using the ASR confidence data. In addition or alternatively, the server may modify the user verification confidence data using at least one of a location of the speech-controlled device within a building, a type of the speech-controlled device, or a geographic location of the speech-controlled device.

    Robust neural network acoustic model with side task prediction of reference signals
    19.
    发明授权
    Robust neural network acoustic model with side task prediction of reference signals 有权

    公开(公告)号：US10147442B1

    公开(公告)日：2018-12-04

    申请号：US14869803

    申请日：2015-09-29

    Applicant: Amazon Technologies, Inc.

    Inventor： Sankaran Panchapagesan ,  Shiva Kumar Sundaram ,  Arindam Mandal

    IPC: G10L25/30 ,  G10L15/02 ,  G10L15/06 ,  G10L25/12 ,  G10L15/20

    Abstract: A neural network acoustic model is trained to be robust and produce accurate output when used to process speech signals having acoustic interference. The neural network acoustic model can be trained using a source-separation process by which, in addition to producing the main acoustic model output for a given input, the neural network generates predictions of the separate speech and interference portions of the input. The parameters of the neural network can be adjusted to jointly optimize all three outputs (e.g., the main acoustic model output, the speech signal prediction, and the interference signal prediction), rather than only optimizing the main acoustic model output. Once trained, output layers for the speech and interference signal predictions can be removed from the neural network or otherwise disabled.

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