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    On-device speech synthesis of textual segments for training of on-device speech recognition model
    31.
    发明授权
    On-device speech synthesis of textual segments for training of on-device speech recognition model 有权

    公开(公告)号：US11978432B2

    公开(公告)日：2024-05-07

    申请号：US18204324

    申请日：2023-05-31

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    CPC classification number: G10L13/047 ,  G10L15/063 ,  G10L2015/0635

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS
    34.
    发明公开
    USING CORRECTIONS, OF AUTOMATED ASSISTANT FUNCTIONS, FOR TRAINING OF ON-DEVICE MACHINE LEARNING MODELS 审中-公开

    公开(公告)号：US20230352019A1

    公开(公告)日：2023-11-02

    申请号：US18218818

    申请日：2023-07-06

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Rajiv Mathews ,  Dragan Zivkovic ,  Kurt Partridge ,  Andrew Hard

    IPC: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    CPC classification number: G10L15/22 ,  G10L15/065 ,  G10L15/10 ,  G10L15/30

    Abstract: Processor(s) of a client device can: receive sensor data that captures environmental attributes of an environment of the client device; process the sensor data using a machine learning model to generate a predicted output that dictates whether one or more currently dormant automated assistant functions are activated; making a decision as to whether to trigger the one or more currently dormant automated assistant functions; subsequent to making the decision, determining that the decision was incorrect; and in response to determining that the determination was incorrect, generating a gradient based on comparing the predicted output to ground truth output. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S)
    35.
    发明公开
    MIXED CLIENT-SERVER FEDERATED LEARNING OF MACHINE LEARNING MODEL(S) 审中-公开

    公开(公告)号：US20230352004A1

    公开(公告)日：2023-11-02

    申请号：US18218319

    申请日：2023-07-05

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Andrew Hard ,  Swaroop Indra Ramaswamy ,  Om Dipakbhai Thakkar ,  Rajiv Mathews

    IPC: G10L15/065 ,  G10L13/04 ,  G10L15/26 ,  G10L15/30

    CPC classification number: G10L15/065 ,  G10L13/04 ,  G10L15/26 ,  G10L15/30

    Abstract: Implementations disclosed herein are directed to federated learning of machine learning (“ML”) model(s) based on gradient(s) generated at corresponding client devices and a remote system. Processor(s) of the corresponding client devices can process client data generated locally at the corresponding client devices using corresponding on-device ML model(s) to generate corresponding predicted outputs, generate corresponding client gradients based on the corresponding predicted outputs, and transmit the corresponding client gradients to the remote system. Processor(s) of the remote system can process remote data obtained from remote database(s) using global ML model(s) to generate additional corresponding predicted outputs, generate corresponding remote gradients based on the additional corresponding predicted outputs. Further, the remote system can utilize the corresponding client gradients and the corresponding remote gradients to update the global ML model(s) or weights thereof. The updated global ML model(s) and/or the updated weights thereof can be transmitted back to the corresponding client devices.

    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    36.
    发明公开
    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 审中-公开

    公开(公告)号：US20230306955A1

    公开(公告)日：2023-09-28

    申请号：US18204324

    申请日：2023-05-31

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    CPC classification number: G10L13/047 ,  G10L15/063 ,  G10L2015/0635

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    On-device speech synthesis of textual segments for training of on-device speech recognition model
    37.
    发明授权
    On-device speech synthesis of textual segments for training of on-device speech recognition model 有权

    公开(公告)号：US11705106B2

    公开(公告)日：2023-07-18

    申请号：US17479285

    申请日：2021-09-20

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    CPC classification number: G10L13/047 ,  G10L15/063 ,  G10L2015/0635

    Abstract: Processor(s) of a client device can: identify a textual segment stored locally at the client device; process the textual segment, using a speech synthesis model stored locally at the client device, to generate synthesized speech audio data that includes synthesized speech of the identified textual segment; process the synthesized speech, using an on-device speech recognition model that is stored locally at the client device, to generate predicted output; and generate a gradient based on comparing the predicted output to ground truth output that corresponds to the textual segment. In some implementations, the generated gradient is used, by processor(s) of the client device, to update weights of the on-device speech recognition model. In some implementations, the generated gradient is additionally or alternatively transmitted to a remote system for use in remote updating of global weights of a global speech recognition model.

    METHOD(S) AND SYSTEM(S) FOR IMPROVED EFFICIENCY IN FEDERATED LEARNING OF MACHINE LEARNING MODEL(S)
    38.
    发明公开
    METHOD(S) AND SYSTEM(S) FOR IMPROVED EFFICIENCY IN FEDERATED LEARNING OF MACHINE LEARNING MODEL(S) 审中-公开

    公开(公告)号：US20230177382A1

    公开(公告)日：2023-06-08

    申请号：US17541091

    申请日：2021-12-02

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Giovanni Motta ,  Khe Chai Sim

    IPC: G06N20/00 ,  G06K9/62 ,  H04L67/10

    CPC classification number: G06N20/00 ,  G06K9/6262 ,  H04L67/10

    Abstract: Implementations disclosed herein are directed to efficient federated learning of machine learning (ML) model(s) at a remote system (e.g., remote server(s)) based on update(s) generated at client device(s). Processor(s) of the client device(s) can receive client data, process, using on-device ML model(s), the client data to generate predicted output(s), generate, using unsupervised learning, gradient(s) based on the predicted output(s), generate, based on the gradient(s), the update(s) for disparate portions of the on-device ML model(s) and/or global ML model(s) that are remote-based counterparts of the on-device ML model(s). Further, processor(s) of the remote system can receive, from the client device(s), the update(s) for the disparate portions of the on-device ML model(s), and cause the global ML model(s) to be updated based on the update(s) for the disparate portions of the on-device ML model(s) received from disparate client device(s). Thus, resources consumed at the client device(s) and/or network resources can be reduced.

    EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S)
    40.
    发明申请
    EVALUATING ON-DEVICE MACHINE LEARNING MODEL(S) BASED ON PERFORMANCE MEASURES OF CLIENT DEVICE(S) AND/OR THE ON-DEVICE MACHINE LEARNING MODEL(S) 有权

    公开(公告)号：US20220309389A1

    公开(公告)日：2022-09-29

    申请号：US17215588

    申请日：2021-03-29

    Applicant: Google LLC

    Inventor： Dragan Zivkovic ,  Akash Agrawal ,  Françoise Beaufays ,  Tamar Lucassen

    IPC: G06N20/00 ,  G06N5/04

    Abstract: Implementations disclosed herein are directed to systems and methods for evaluating on-device machine learning (ML) model(s) based on performance measure(s) of client device(s) and/or the on-device ML model(s). The client device(s) can include on-device memory that stores the on-device ML model(s) and a plurality of testing instances for the on-device ML model(s). When certain condition(s) are satisfied, the client device(s) can process, using the on-device ML model(s), the plurality of testing instances to generate the performance measure(s). The performance measure(s) can include, for example, latency measure(s), memory consumption measure(s), CPU usage measure(s), ML model measure(s) (e.g., precision and/or recall), and/or other measures. In some implementations, the on-device ML model(s) can be activated (or kept active) for use locally at the client device(s) based on the performance measure(s). In other implementations, the on-device ML model(s) can be sparsified based on the performance measure(s).

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