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    Proper noun recognition in end-to-end speech recognition
    1.
    发明授权
    Proper noun recognition in end-to-end speech recognition 有权

    公开(公告)号：US11749259B2

    公开(公告)日：2023-09-05

    申请号：US17150491

    申请日：2021-01-15

    Applicant: Google LLC

    Inventor： Charles Caleb Peyser ,  Tara N. Sainath ,  Golan Pundak

    IPC: G10L15/06 ,  G10L15/16 ,  G10L15/18 ,  G10L15/187 ,  G06N3/049

    CPC classification number: G10L15/063 ,  G06N3/049 ,  G10L15/16 ,  G10L15/187 ,  G10L15/1815

    Abstract: A method for training a speech recognition model with a minimum word error rate loss function includes receiving a training example comprising a proper noun and generating a plurality of hypotheses corresponding to the training example. Each hypothesis of the plurality of hypotheses represents the proper noun and includes a corresponding probability that indicates a likelihood that the hypothesis represents the proper noun. The method also includes determining that the corresponding probability associated with one of the plurality of hypotheses satisfies a penalty criteria. The penalty criteria indicating that the corresponding probability satisfies a probability threshold, and the associated hypothesis incorrectly represents the proper noun. The method also includes applying a penalty to the minimum word error rate loss function.

    RESOLVING UNIQUE PERSONAL IDENTIFIERS DURING CORRESPONDING CONVERSATIONS BETWEEN A VOICE BOT AND A HUMAN
    2.
    发明申请
    RESOLVING UNIQUE PERSONAL IDENTIFIERS DURING CORRESPONDING CONVERSATIONS BETWEEN A VOICE BOT AND A HUMAN 有权

    公开(公告)号：US20220238105A1

    公开(公告)日：2022-07-28

    申请号：US17157207

    申请日：2021-01-25

    Applicant: GOOGLE LLC

    Inventor： Rafael Goldfarb ,  Or Guz ,  Lior Alon ,  Assaf Hurwitz Michaely ,  Golan Pundak ,  Shmuel Leibtag ,  Tomer Amiaz ,  Dan Rasin ,  Asaf Aharoni

    IPC: G10L15/22 ,  G10L15/06

    Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.

    ENHANCING AUDIO USING MULTIPLE RECORDING DEVICES
    4.
    发明申请
    ENHANCING AUDIO USING MULTIPLE RECORDING DEVICES 审中-公开

    公开(公告)号：US20200211597A1

    公开(公告)日：2020-07-02

    申请号：US16812760

    申请日：2020-03-09

    Applicant: Google LLC

    Inventor： Dimitri Kanevsky ,  Golan Pundak

    IPC: G11B20/10 ,  G06F3/16 ,  G10L17/00 ,  G10L21/0364 ,  H04M3/56 ,  G10L25/51 ,  G10L21/02

    Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying that a first audio stream includes first, second, and third sources of audio. A computing system identifies that a second audio stream includes the first, second, and third sources of audio. The computing system determines that the first and second sources of audio are part of a first conversation. The computing system generates a third audio stream that combines the first source of audio from the first audio stream, the first source of audio from the second audio stream, the second source of audio from the first audio stream, and the second source of audio from the second audio stream, and diminishes the third source of audio from the first audio stream, and the third source of audio from the second audio stream.

    Contextual biasing for speech recognition
    5.
    发明授权
    Contextual biasing for speech recognition 有权

    公开(公告)号：US12051407B2

    公开(公告)日：2024-07-30

    申请号：US17815049

    申请日：2022-07-26

    Applicant: Google LLC

    Inventor： Rohit Prakash Prabhavalkar ,  Golan Pundak ,  Tara N. Sainath

    IPC: G10L15/16 ,  G10L15/26

    CPC classification number: G10L15/16 ,  G10L15/26

    Abstract: A method includes receiving audio data encoding an utterance and obtaining a set of bias phrases corresponding to a context of the utterance. Each bias phrase includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio to generate an output from the speech recognition model. The speech recognition model includes a first encoder configured to receive the acoustic features, a bias encoder configured to receive data indicating the obtained set of bias phrases, a bias encoder, and a decoder configured to determine likelihoods of sequences of speech elements based on output of the first attention module and output of the bias attention module. The method also includes determining a transcript for the utterance based on the likelihoods of sequences of speech elements.

    Contextual Biasing for Speech Recognition
    6.
    发明申请
    Contextual Biasing for Speech Recognition 有权

    公开(公告)号：US20220101836A1

    公开(公告)日：2022-03-31

    申请号：US17643423

    申请日：2021-12-09

    Applicant: Google LLC

    Inventor： Rohit Prakash Prabhavalkar ,  Golan Pundak ,  Tara N. Sainath ,  Antoine Jean Bruguier

    IPC: G10L15/187 ,  G06N20/10 ,  G10L19/04

    Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.

    Proper Noun Recognition in End-to-End Speech Recognition
    7.
    发明申请
    Proper Noun Recognition in End-to-End Speech Recognition 有权

    公开(公告)号：US20210233512A1

    公开(公告)日：2021-07-29

    申请号：US17150491

    申请日：2021-01-15

    Applicant: Google LLC

    Inventor： Charles Caleb Peyser ,  Tara N. Sainath ,  Golan Pundak

    IPC: G10L15/06 ,  G06N3/04 ,  G10L15/16 ,  G10L15/18 ,  G10L15/187

    Abstract: A method for training a speech recognition model with a minimum word error rate loss function includes receiving a training example comprising a proper noun and generating a plurality of hypotheses corresponding to the training example. Each hypothesis of the plurality of hypotheses represents the proper noun and includes a corresponding probability that indicates a likelihood that the hypothesis represents the proper noun. The method also includes determining that the corresponding probability associated with one of the plurality of hypotheses satisfies a penalty criteria. The penalty criteria indicating that the corresponding probability satisfies a probability threshold, and the associated hypothesis incorrectly represents the proper noun. The method also includes applying a penalty to the minimum word error rate loss function.

    CONTEXTUAL BIASING FOR SPEECH RECOGNITION
    10.
    发明申请
    CONTEXTUAL BIASING FOR SPEECH RECOGNITION 有权

    公开(公告)号：US20240379095A1

    公开(公告)日：2024-11-14

    申请号：US18782001

    申请日：2024-07-23

    Applicant: Google LLC

    Inventor： Rohit Prakash Prabhavalkar ,  Golan Pundak ,  Tara N. Sainath

    IPC: G10L15/16 ,  G10L15/26

    Abstract: A method includes receiving audio data encoding an utterance and obtaining a set of bias phrases corresponding to a context of the utterance. Each bias phrase includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio to generate an output from the speech recognition model. The speech recognition model includes a first encoder configured to receive the acoustic features, a bias encoder configured to receive data indicating the obtained set of bias phrases, a bias encoder, and a decoder configured to determine likelihoods of sequences of speech elements based on output of the first attention module and output of the bias attention module. The method also includes determining a transcript for the utterance based on the likelihoods of sequences of speech elements.

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