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    MIXTURE-OF-EXPERT CONFORMER FOR STREAMING MULTILINGUAL ASR
    1.
    发明公开
    MIXTURE-OF-EXPERT CONFORMER FOR STREAMING MULTILINGUAL ASR 审中-公开

    公开(公告)号：US20240304185A1

    公开(公告)日：2024-09-12

    申请号：US18598885

    申请日：2024-03-07

    Applicant: Google LLC

    Inventor： Ke Hu ,  Bo Li ,  Tara N. Sainath ,  Yu Zhang ,  Francoise Beaufays

    IPC: G10L15/197 ,  G10L15/02 ,  G10L15/06

    CPC classification number: G10L15/197 ,  G10L15/02 ,  G10L15/063

    Abstract: A method of a multilingual ASR model includes receiving a sequence of acoustic frames characterizing an utterance of speech. At a plurality of output steps, the method further includes generating a first higher order feature representation for an acoustic frame by a first encoder that includes a first plurality of multi-head attention layers; generating a second higher order feature representation for a corresponding first higher order feature representation by a second encoder that includes a second plurality of multi-head attention layers; and generating, by a first decoder, a first probability distribution over possible speech recognition hypotheses based on the second higher order feature representation and a sequence of N previous non-blank symbols. A gating layer of each respective MoE layer configured to dynamically route an output from a previous multi-head attention layer at each of the plurality of output steps to a respective pair of feed-forward expert networks.

    Deliberation by Text-Only and Semi-Supervised Training
    3.
    发明公开
    Deliberation by Text-Only and Semi-Supervised Training 审中-公开

    公开(公告)号：US20230298563A1

    公开(公告)日：2023-09-21

    申请号：US18186157

    申请日：2023-03-18

    Applicant: Google LLC

    Inventor： Ke Hu ,  Tara N. Sainath ,  Yanzhang He ,  Rohit Prabhavalkar ,  Sepand Mavandadi ,  Weiran Wang ,  Trevor Strohman

    IPC: G10L13/08 ,  G10L15/16 ,  G10L15/06

    CPC classification number: G10L13/08 ,  G10L15/16 ,  G10L15/063

    Abstract: A method of text-only and semi-supervised training for deliberation includes receiving training data including unspoken textual utterances that are each not paired with any corresponding spoken utterance of non-synthetic speech, and training a deliberation model that includes a text encoder and a deliberation decoder on the unspoken textual utterances. The method also includes receiving, at the trained deliberation model, first-pass hypotheses and non-causal acoustic embeddings. The first-pass hypotheses is generated by a recurrent neural network-transducer (RNN-T) decoder for the non-causal acoustic embeddings encoded by a non-causal encoder. The method also includes encoding, using the text encoder, the first-pass hypotheses generated by the RNN-T decoder, and generating, using the deliberation decoder attending to both the first-pass hypotheses and the non-causal acoustic embeddings, second-pass hypotheses.

    Learning Word-Level Confidence for Subword End-To-End Automatic Speech Recognition
    4.
    发明申请
    Learning Word-Level Confidence for Subword End-To-End Automatic Speech Recognition 有权

    公开(公告)号：US20220270597A1

    公开(公告)日：2022-08-25

    申请号：US17182592

    申请日：2021-02-23

    Applicant: Google LLC

    Inventor： David Qiu ,  Qiujia Li ,  Yanzhang He ,  Yu Zhang ,  Bo Li ,  Liangliang Cao ,  Rohit Prabhavalkar ,  Deepti Bhatia ,  Wei Li ,  Ke Hu ,  Tara Sainath ,  Ian Mcgraw

    IPC: G10L15/22 ,  G10L15/08 ,  G10L25/30 ,  G06N3/08

    Abstract: A method includes receiving a speech recognition result, and using a confidence estimation module (CEM), for each sub-word unit in a sequence of hypothesized sub-word units for the speech recognition result: obtaining a respective confidence embedding that represents a set of confidence features; generating, using a first attention mechanism, a confidence feature vector; generating, using a second attention mechanism, an acoustic context vector; and generating, as output from an output layer of the CEM, a respective confidence output score for each corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the CEM. For each of the one or more words formed by the sequence of hypothesized sub-word units, the method also includes determining a respective word-level confidence score for the word. The method also includes determining an utterance-level confidence score by aggregating the word-level confidence scores.

    MULTILINGUAL AND CODE-SWITCHING ASR USING LARGE LANGUAGE MODEL GENERATED TEXT
    9.
    发明申请
    MULTILINGUAL AND CODE-SWITCHING ASR USING LARGE LANGUAGE MODEL GENERATED TEXT 有权

    公开(公告)号：US20250095637A1

    公开(公告)日：2025-03-20

    申请号：US18886581

    申请日：2024-09-16

    Applicant: Google LLC

    Inventor： Ke Hu ,  Tara N. Sainath ,  Bo Li ,  Yu Zhang ,  Yong Cheng ,  Tao Wang ,  Yujing Zhang ,  Frederick Liu

    IPC: G10L15/06 ,  G10L15/00

    Abstract: A method includes receiving a textual prompt in a first language and obtaining a fine-tuned prompt embedding configured to guide a large language model (LLM) to generate text in a target language from textual prompts in the first language. The method also includes processing, using the LLM, the textual prompt conditioned on the fine-tuned prompt embedding to generate output text in the target language and concatenating the textual prompt and the generated output text to provide an unspoken textual utterance. The method also includes training a multilingual automatic speech recognition (ASR) model to learn how to recognize speech in the target language by injecting the unspoken textual utterance into a text encoder associated with the multilingual ASR model.

    Transducer-Based Streaming Deliberation for Cascaded Encoders
    10.
    发明申请
    Transducer-Based Streaming Deliberation for Cascaded Encoders 有权

    公开(公告)号：US20240428786A1

    公开(公告)日：2024-12-26

    申请号：US18826655

    申请日：2024-09-06

    Applicant: Google LLC

    Inventor： Ke Hu ,  Tara N. Sainath ,  Arun Narayanan ,  Ruoming Pang ,  Trevor Strohman

    IPC: G10L15/197 ,  G06F40/126 ,  G10L15/02 ,  G10L15/06 ,  G10L15/08 ,  G10L15/22

    Abstract: A method includes receiving a sequence of acoustic frames and generating, by a first encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by a first pass transducer decoder, a first pass speech recognition hypothesis for a corresponding first higher order feature representation and generating, by a text encoder, a text encoding for a corresponding first pass speech recognition hypothesis. The method also includes generating, by a second encoder, a second higher order feature representation for a corresponding first higher order feature representation. The method also includes generating, by a second pass transducer decoder, a second pass speech recognition hypothesis using a corresponding second higher order feature representation and a corresponding text encoding.

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