Speech recognition using neural networks

    公开(公告)号:US12243515B2

    公开(公告)日:2025-03-04

    申请号:US18177717

    申请日:2023-03-02

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

    User interface customization based on speaker characteristics

    公开(公告)号:US11137977B2

    公开(公告)日:2021-10-05

    申请号:US15230891

    申请日:2016-08-08

    Applicant: Google LLC

    Abstract: The characteristics of the speaker may be used to automatically customize a user interface of a client device for the speaker. For instance, a mobile device may generate a user interface that does not permit access to particular applications on the mobile device, that only includes options to call home and access a camera application, or that includes an override option to cause generation of a user interface for a different class of user than a user class for the automatic customizations applied to the user interface.

    SPEECH RECOGNITION USING NEURAL NETWORKS
    4.
    发明公开

    公开(公告)号:US20230206909A1

    公开(公告)日:2023-06-29

    申请号:US18177717

    申请日:2023-03-02

    Applicant: Google LLC

    CPC classification number: G10L15/16 G06N3/02 G10L15/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

    Speech recognition using neural networks

    公开(公告)号:US11620991B2

    公开(公告)日:2023-04-04

    申请号:US17154376

    申请日:2021-01-21

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

    SPEECH RECOGNITION USING NEURAL NETWORKS

    公开(公告)号:US20210183376A1

    公开(公告)日:2021-06-17

    申请号:US17154376

    申请日:2021-01-21

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

    Speech recognition using neural networks

    公开(公告)号:US10438581B2

    公开(公告)日:2019-10-08

    申请号:US13955483

    申请日:2013-07-31

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

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