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    TWO-PASS END TO END SPEECH RECOGNITION
    53.
    发明申请
    TWO-PASS END TO END SPEECH RECOGNITION 有权

    公开(公告)号：US20240420687A1

    公开(公告)日：2024-12-19

    申请号：US18815537

    申请日：2024-08-26

    Applicant: GOOGLE LLC

    Inventor： Tara N. Sainath ,  Yanzhang He ,  Bo Li ,  Arun Narayanan ,  Ruoming Pang ,  Antoine Jean Bruguier ,  Shuo-yiin Chang ,  Wei Li

    IPC: G10L15/16 ,  G06N3/08 ,  G10L15/05 ,  G10L15/06 ,  G10L15/22

    Abstract: Two-pass automatic speech recognition (ASR) models can be used to perform streaming on-device ASR to generate a text representation of an utterance captured in audio data. Various implementations include a first-pass portion of the ASR model used to generate streaming candidate recognition(s) of an utterance captured in audio data. For example, the first-pass portion can include a recurrent neural network transformer (RNN-T) decoder. Various implementations include a second-pass portion of the ASR model used to revise the streaming candidate recognition(s) of the utterance and generate a text representation of the utterance. For example, the second-pass portion can include a listen attend spell (LAS) decoder. Various implementations include a shared encoder shared between the RNN-T decoder and the LAS decoder.

    Cascaded encoders for simplified streaming and non-streaming ASR
    54.
    发明授权
    Cascaded encoders for simplified streaming and non-streaming ASR 有权

    公开(公告)号：US12154581B2

    公开(公告)日：2024-11-26

    申请号：US17237021

    申请日：2021-04-21

    Applicant: Google LLC

    Inventor： Arun Narayanan ,  Tara Sainath ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Rohit Prabhavalkar ,  Jiahui Yu ,  Ehsan Variani ,  Trevor Strohman

    IPC: G10L19/16 ,  G06N3/08 ,  G10L15/00 ,  G10L15/16 ,  G10L15/32 ,  G10L25/30

    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.

    Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training
    55.
    发明公开
    Contrastive Learning and Masked Modeling for End-To-End Self-Supervised Pre-Training 审中-公开

    公开(公告)号：US20240104352A1

    公开(公告)日：2024-03-28

    申请号：US18012391

    申请日：2022-07-28

    Applicant: Google LLC

    Inventor： Yu Zhang ,  Yu-An Chung ,  Wei Han ,  Chung-Cheng Chiu ,  Weikeng Qin ,  Ruoming Pang ,  Yonghui Wu

    IPC: G06N3/0455

    CPC classification number: G06N3/0455

    Abstract: Provided are improved end-to-end self-supervised pre-training frameworks that leverage a combination of contrastive and masked modeling loss terms. In particular, the present disclosure provides framework that combines contrastive learning and masked modeling, where the former trains the model to discretize input data (e.g., continuous signals such as continuous speech signals) into a finite set of discriminative tokens, and the latter trains the model to learn contextualized representations via solving a masked prediction task consuming the discretized tokens. In contrast to certain existing masked modeling-based pre-training frameworks which rely on an iterative re-clustering and re-training process or other existing frameworks which concatenate two separately trained modules, the proposed framework can enable a model to be optimized in an end-to-end fashion by solving the two self-supervised tasks (the contrastive task and masked modeling) simultaneously.

    Neural Architecture Search with Factorized Hierarchical Search Space
    59.
    发明公开
    Neural Architecture Search with Factorized Hierarchical Search Space 审中-公开

    公开(公告)号：US20230244904A1

    公开(公告)日：2023-08-03

    申请号：US18154321

    申请日：2023-01-13

    Applicant: Google LLC

    Inventor： Mingxing Tan ,  Quoc Le ,  Bo Chen ,  Vijay Vasudevan ,  Ruoming Pang

    IPC: G06N3/04 ,  G06N20/10 ,  G06F17/15 ,  G06N3/084 ,  G06N3/044

    CPC classification number: G06N3/04 ,  G06N20/10 ,  G06F17/15 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

    Abstract: The present disclosure is directed to an automated neural architecture search approach for designing new neural network architectures such as, for example, resource-constrained mobile CNN models. In particular, the present disclosure provides systems and methods to perform neural architecture search using a novel factorized hierarchical search space that permits layer diversity throughout the network, thereby striking the right balance between flexibility and search space size. The resulting neural architectures are able to be run relatively faster and using relatively fewer computing resources (e.g., less processing power, less memory usage, less power consumption, etc.), all while remaining competitive with or even exceeding the performance (e.g., accuracy) of current state-of-the-art mobile-optimized models.

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