公开(公告)号：US12051404B2

公开(公告)日：2024-07-30

申请号：US18336211

申请日：2023-06-16

Applicant: Google LLC

Inventor： Tara Sainath ,  Arun Narayanan ,  Rami Botros ,  Yanzhang He ,  Ehsan Variani ,  Cyril Allauzen ,  David Rybach ,  Ruoming Pang ,  Trevor Strohman

IPC: G10L15/00 ,  G10L15/02 ,  G10L15/06 ,  G10L15/22 ,  G10L15/30

CPC classification number: G10L15/063 ,  G10L15/02 ,  G10L15/22 ,  G10L15/30

Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.

公开(公告)号：US20230352006A1

公开(公告)日：2023-11-02

申请号：US18347842

申请日：2023-07-06

Applicant: Google LLC

Inventor： Rami Botros ,  Tara Sainath

IPC: G10L15/16 ,  G10L15/08

CPC classification number: G10L15/16 ,  G10L15/083

Abstract: A RNN-T model includes a prediction network configured to, at each of a plurality of times steps subsequent to an initial time step, receive a sequence of non-blank symbols. For each non-blank symbol the prediction network is also configured to generate, using a shared embedding matrix, an embedding of the corresponding non-blank symbol, assign a respective position vector to the corresponding non-blank symbol, and weight the embedding proportional to a similarity between the embedding and the respective position vector. The prediction network is also configured to generate a single embedding vector at the corresponding time step. The RNN-T model also includes a joint network configured to, at each of the plurality of time steps subsequent to the initial time step, receive the single embedding vector generated as output from the prediction network at the corresponding time step and generate a probability distribution over possible speech recognition hypotheses.

公开(公告)号：US20220310062A1

公开(公告)日：2022-09-29

申请号：US17316198

申请日：2021-05-10

Applicant: Google LLC

Inventor： Tara Sainath ,  Arun Narayanan ,  Rami Botros ,  Yangzhang He ,  Ehsan Variani ,  Cyrill Allauzen ,  David Rybach ,  Ruorning Pang ,  Trevor Strohman

IPC: G10L15/06 ,  G10L15/02 ,  G10L15/30 ,  G10L15/22

Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.