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    Multi-dialect and multilingual speech recognition
    2.
    发明授权
    Multi-dialect and multilingual speech recognition 有权

    公开(公告)号：US12254865B2

    公开(公告)日：2025-03-18

    申请号：US18418246

    申请日：2024-01-20

    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 ,  G10L15/06 ,  G10L15/07 ,  G10L15/16

    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.

    Emitting word timings with end-to-end models
    4.
    发明授权
    Emitting word timings with end-to-end models 有权

    公开(公告)号：US12027154B2

    公开(公告)日：2024-07-02

    申请号：US18167050

    申请日：2023-02-09

    Applicant: Google LLC

    Inventor： Tara N. Sainath ,  Basilio Garcia Castillo ,  David Rybach ,  Trevor Strohman ,  Ruoming Pang

    IPC: G10L25/30 ,  G10L15/06 ,  G10L25/78

    CPC classification number: G10L15/063 ,  G10L25/30 ,  G10L25/78

    Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.

    Large-scale language model data selection for rare-word speech recognition
    5.
    发明授权
    Large-scale language model data selection for rare-word speech recognition 有权

    公开(公告)号：US12014725B2

    公开(公告)日：2024-06-18

    申请号：US17643861

    申请日：2021-12-13

    Applicant: Google LLC

    Inventor： Ronny Huang ,  Tara N. Sainath

    IPC: G10L15/16 ,  G06N3/02 ,  G10L15/06 ,  G10L15/197 ,  G10L15/22

    CPC classification number: G10L15/063 ,  G06N3/02 ,  G10L15/16 ,  G10L15/197 ,  G10L15/22

    Abstract: A method of training a language model for rare-word speech recognition includes obtaining a set of training text samples, and obtaining a set of training utterances used for training a speech recognition model. Each training utterance in the plurality of training utterances includes audio data corresponding to an utterance and a corresponding transcription of the utterance. The method also includes applying rare word filtering on the set of training text samples to identify a subset of rare-word training text samples that include words that do not appear in the transcriptions from the set of training utterances or appear in the transcriptions from the set of training utterances less than a threshold number of times. The method further includes training the external language model on the transcriptions from the set of training utterances and the identified subset of rare-word training text samples.

    Unified End-To-End Speech Recognition And Endpointing Using A Switch Connection
    9.
    发明公开
    Unified End-To-End Speech Recognition And Endpointing Using A Switch Connection 审中-公开

    公开(公告)号：US20240029719A1

    公开(公告)日：2024-01-25

    申请号：US18340093

    申请日：2023-06-23

    Applicant: Google LLC

    Inventor： Shaan Jagdeep Patrick Bijwadia ,  Shuo-yiin Chang ,  Bo Li ,  Yanzhang He ,  Tara N. Sainath ,  Chao Zhang

    IPC: G10L15/16 ,  G10L15/06 ,  G10L25/93

    CPC classification number: G10L15/16 ,  G10L15/063 ,  G10L25/93

    Abstract: A single E2E multitask model includes a speech recognition model and an endpointer model. The speech recognition model includes an audio encoder configured to encode a sequence of audio frames into corresponding higher-order feature representations, and a decoder configured to generate probability distributions over possible speech recognition hypotheses for the sequence of audio frames based on the higher-order feature representations. The endpointer model is configured to operate between a VAD mode and an EOQ detection mode. During the VAD mode, the endpointer model receives input audio frames, and determines, for each input audio frame, whether the input audio frame includes speech. During the EOQ detection mode, the endpointer model receives latent representations for the sequence of audio frames output from the audio encoder, and determines, for each of the latent representation, whether the latent representation includes final silence.

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