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    Training and/or using a language selection model for automatically determining language for speech recognition of spoken utterance
    3.
    发明授权
    Training and/or using a language selection model for automatically determining language for speech recognition of spoken utterance 有权

    公开(公告)号：US11646011B2

    公开(公告)日：2023-05-09

    申请号：US17846287

    申请日：2022-06-22

    Applicant: Google LLC

    Inventor： Li Wan ,  Yang Yu ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Quan Wang

    IPC: G10L15/00

    CPC classification number: G10L15/005

    Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.

    TRAINING AND/OR USING A LANGUAGE SELECTION MODEL FOR AUTOMATICALLY DETERMINING LANGUAGE FOR SPEECH RECOGNITION OF SPOKEN UTTERANCE
    4.
    发明申请
    TRAINING AND/OR USING A LANGUAGE SELECTION MODEL FOR AUTOMATICALLY DETERMINING LANGUAGE FOR SPEECH RECOGNITION OF SPOKEN UTTERANCE 审中-公开

    公开(公告)号：US20200335083A1

    公开(公告)日：2020-10-22

    申请号：US16959037

    申请日：2019-11-27

    Applicant: Google LLC

    Inventor： Li Wan ,  Yang Yu ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Quan Wang

    IPC: G10L15/00

    Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.

    TARGETED VOICE SEPARATION BY SPEAKER CONDITIONED ON SPECTROGRAM MASKING
    5.
    发明申请
    TARGETED VOICE SEPARATION BY SPEAKER CONDITIONED ON SPECTROGRAM MASKING 有权

    公开(公告)号：US20220122611A1

    公开(公告)日：2022-04-21

    申请号：US17567590

    申请日：2022-01-03

    Applicant: GOOGLE LLC

    Inventor： Quan Wang ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Hannah Muckenhirn

    IPC: G10L17/04 ,  G10L17/22 ,  G10L25/18 ,  G10L17/02 ,  G10L17/18 ,  G10L17/00

    Abstract: Techniques are disclosed that enable processing of audio data to generate one or more refined versions of audio data, where each of the refined versions of audio data isolate one or more utterances of a single respective human speaker. Various implementations generate a refined version of audio data that isolates utterance(s) of a single human speaker by processing a spectrogram representation of the audio data (generated by processing the audio data with a frequency transformation) using a mask generated by processing the spectrogram of the audio data and a speaker embedding for the single human speaker using a trained voice filter model. Output generated over the trained voice filter model is processed using an inverse of the frequency transformation to generate the refined audio data.

    Targeted voice separation by speaker conditioned on spectrogram masking
    6.
    发明授权
    Targeted voice separation by speaker conditioned on spectrogram masking 有权

    公开(公告)号：US11217254B2

    公开(公告)日：2022-01-04

    申请号：US16598172

    申请日：2019-10-10

    Applicant: Google LLC

    Inventor： Quan Wang ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Hannah Muckenhirn

    IPC: G10L17/04 ,  G10L17/22 ,  G10L25/18 ,  G10L17/02 ,  G10L17/18 ,  G10L17/00

    Abstract: Techniques are disclosed that enable processing of audio data to generate one or more refined versions of audio data, where each of the refined versions of audio data isolate one or more utterances of a single respective human speaker. Various implementations generate a refined version of audio data that isolates utterance(s) of a single human speaker by processing a spectrogram representation of the audio data (generated by processing the audio data with a frequency transformation) using a mask generated by processing the spectrogram of the audio data and a speaker embedding for the single human speaker using a trained voice filter model. Output generated over the trained voice filter model is processed using an inverse of the frequency transformation to generate the refined audio data.

    TRAINING AND/OR USING A LANGUAGE SELECTION MODEL FOR AUTOMATICALLY DETERMINING LANGUAGE FOR SPEECH RECOGNITION OF SPOKEN UTTERANCE
    7.
    发明申请
    TRAINING AND/OR USING A LANGUAGE SELECTION MODEL FOR AUTOMATICALLY DETERMINING LANGUAGE FOR SPEECH RECOGNITION OF SPOKEN UTTERANCE 有权

    公开(公告)号：US20220328035A1

    公开(公告)日：2022-10-13

    申请号：US17846287

    申请日：2022-06-22

    Applicant: Google LLC

    Inventor： Li Wan ,  Yang Yu ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Quan Wang

    IPC: G10L15/00

    Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.

    Training and/or using a language selection model for automatically determining language for speech recognition of spoken utterance
    8.
    发明授权
    Training and/or using a language selection model for automatically determining language for speech recognition of spoken utterance 有权

    公开(公告)号：US11410641B2

    公开(公告)日：2022-08-09

    申请号：US16959037

    申请日：2019-11-27

    Applicant: Google LLC

    Inventor： Li Wan ,  Yang Yu ,  Prashant Sridhar ,  Ignacio Lopez Moreno ,  Quan Wang

    IPC: G10L15/00

    Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.

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