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    EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S)
    11.
    发明公开
    EPHEMERAL LEARNING OF MACHINE LEARNING MODEL(S) 审中-公开

    公开(公告)号：US20230156248A1

    公开(公告)日：2023-05-18

    申请号：US17533779

    申请日：2021-11-23

    Applicant: GOOGLE LLC

    Inventor： Françoise Beaufays ,  Khe Chai Sim ,  Trevor Strohman ,  Oren Litvin

    IPC: H04N21/233 ,  G06N20/00 ,  G06K9/62 ,  H04N21/232

    CPC classification number: H04N21/233 ,  G06N20/00 ,  G06K9/6256 ,  H04N21/232

    Abstract: Implementations disclosed herein are directed to ephemeral learning of machine learning (“ML”) model(s) based on gradient(s) generated at a remote system (e.g., remote server(s)). Processor(s) of the remote system can receive stream(s) of audio data capturing spoken utterance(s) from a client device of a user. A fulfillment pipeline can process the stream(s) of audio data to cause certain fulfillment(s) of the spoken utterance(s) to be performed. Meanwhile, a training pipeline can process the stream(s) of audio data to generate gradient(s) using unsupervised learning techniques. Subsequent to the processing by the fulfillment pipeline and/or the training pipeline, the stream(s) of audio data are discarded by the remote system. Accordingly, the ML model(s) can be trained at the remote system without storing or logging of the stream(s) of audio data by non-transient memory thereof, thereby providing more efficient training mechanisms for training the ML model(s) and also increasing security of user data.

    MULTI-STREAM RECURRENT NEURAL NETWORK TRANSDUCER(S)
    12.
    发明申请
    MULTI-STREAM RECURRENT NEURAL NETWORK TRANSDUCER(S) 有权

    公开(公告)号：US20220405549A1

    公开(公告)日：2022-12-22

    申请号：US17619643

    申请日：2020-12-15

    Applicant: GOOGLE LLC

    Inventor： Khe Chai Sim ,  Françoise Beaufays

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Techniques are disclosed that enable generating jointly probable output by processing input using a multi-stream recurrent neural network transducer (MS RNN-T) model. Various implementations include generating a first output sequence and a second output sequence by processing a single input sequence using the MS RNN-T, where the first output sequence is jointly probable with the second output sequence. Additional or alternative techniques are disclosed that enable generating output by processing multiple input sequences using the MS RNN-T. Various implementations include processing a first input sequence and a second input sequence using the MS RNN-T to generate output. In some implementations, the MS RNN-T can be used to process two or more input sequences to generate two or more jointly probable output sequences.

    SOUND MODEL LOCALIZATION WITHIN AN ENVIRONMENT
    13.
    发明申请
    SOUND MODEL LOCALIZATION WITHIN AN ENVIRONMENT 有权

    公开(公告)号：US20220027725A1

    公开(公告)日：2022-01-27

    申请号：US16940294

    申请日：2020-07-27

    Applicant: GOOGLE LLC

    Inventor： Rajeev Conrad Nongpiur ,  Byungchul Kim ,  Marie Vachovsky ,  Monica Song ,  Khe Chai Sim ,  Qian Zhang

    IPC: G06N3/08 ,  G10L25/51 ,  G06N3/04

    Abstract: Systems and techniques are provided for sound model localization within an environment. Sound recordings of sounds in the environment may be received from devices in the environment. Preliminary labels for the sound recordings may be determined using pre-trained sound models. The preliminary labels may have associated probabilities. Sound clips with preliminary labels may be generated based on sound recordings that have preliminary labels whose probability is over a high-recall threshold for the pre-trained sound model that determined the preliminary label. The sound clips with preliminary labels may be sent to a user device. Labeled sound clips may be received from the user device. The labeled sound clips may be based on the sound clips with preliminary labels. Training data sets may be generated for the pre-trained sound models using the labeled sound clips. The pre-trained sound models may be trained using the training data sets to generate localized sound models.

    On-device speech synthesis of textual segments for training of on-device speech recognition model
    14.
    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：US11127392B2

    公开(公告)日：2021-09-21

    申请号：US16959546

    申请日：2019-10-02

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/00 ,  G10L13/047 ,  G10L15/06

    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.

    Generation and utilization of pseudo-correction(s) to prevent forgetting of personalized on-device automatic speech recognition (ASR) model(s)
    15.
    发明授权
    Generation and utilization of pseudo-correction(s) to prevent forgetting of personalized on-device automatic speech recognition (ASR) model(s) 有权

    公开(公告)号：US12223952B2

    公开(公告)日：2025-02-11

    申请号：US17959637

    申请日：2022-10-04

    Applicant: GOOGLE LLC

    Inventor： Rajiv Mathews ,  Dragan Zivkovic ,  Khe Chai Sim

    IPC: G10L15/00 ,  G10L15/06 ,  G10L15/19 ,  G10L15/22 ,  G10L15/30

    Abstract: On-device processor(s) of a client device may store, in on-device storage and in association with a time to live (TTL) in the on-device storage, a correction directed to ASR processing of audio data. The correction may include a portion of a given speech hypothesis that was modified to an alternate speech hypothesis. Further, the on-device processor(s) may cause an on-device ASR model to be personalized based on the correction. Moreover, and based on additional ASR processing of additional audio data, the on-device processor(s) may store, in the on-device storage and in association with an additional TTL in the on-device storage, a pseudo-correction directed to the additional ASR processing. Accordingly, the on-device processor(s) may cause the on-device ASR model to be personalized based on the pseudo-correction to prevent forgetting by the on-device ASR model.

    Unsupervised federated learning of machine learning model layers
    16.
    发明授权
    Unsupervised federated learning of machine learning model layers 有权

    公开(公告)号：US12014724B2

    公开(公告)日：2024-06-18

    申请号：US16973605

    申请日：2020-07-20

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Khe Chai Sim ,  Johan Schalkwyk

    IPC: G10L15/06 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30

    CPC classification number: G10L15/063 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/0635

    Abstract: Implementations disclosed herein are directed to unsupervised federated training of global machine learning (“ML”) model layers that, after the federated training, can be combined with additional layer(s), thereby resulting in a combined ML model. Processor(s) can: detect audio data that captures a spoken utterance of a user of a client device; process, using a local ML model, the audio data to generate predicted output(s); generate, using unsupervised learning locally at the client device, a gradient based on the predicted output(s); transmit the gradient to a remote system; update weight(s) of the global ML model layers based on the gradient; subsequent to updating the weight(s), train, using supervised learning remotely at the remote system, a combined ML model that includes the updated global ML model layers and additional layer(s); transmit the combined ML model to the client device; and use the combined ML model to make prediction(s) at the client device.

    Multi-dialect and multilingual speech recognition
    18.
    发明授权
    Multi-dialect and multilingual speech recognition 有权

    公开(公告)号：US11238845B2

    公开(公告)日：2022-02-01

    申请号：US16684483

    申请日：2019-11-14

    Applicant: GOOGLE LLC

    Inventor： Zhifeng Chen ,  Bo Li ,  Eugene Weinstein ,  Yonghui Wu ,  Pedro J. Moreno Mengibar ,  Ron J. Weiss ,  Khe Chai Sim ,  Tara N. Sainath ,  Patrick An Phu Nguyen

    IPC: G10L15/00 ,  G10L15/07 ,  G10L15/16 ,  G10L15/06

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.

    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL
    19.
    发明申请
    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 有权

    公开(公告)号：US20220005458A1

    公开(公告)日：2022-01-06

    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

    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
    20.
    发明申请
    ON-DEVICE SPEECH SYNTHESIS OF TEXTUAL SEGMENTS FOR TRAINING OF ON-DEVICE SPEECH RECOGNITION MODEL 有权

    公开(公告)号：US20210104223A1

    公开(公告)日：2021-04-08

    申请号：US16959546

    申请日：2019-10-02

    Applicant: Google LLC

    Inventor： Françoise Beaufays ,  Johan Schalkwyk ,  Khe Chai Sim

    IPC: G10L13/047 ,  G10L15/06

    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.

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