公开(公告)号：US20220343894A1

公开(公告)日：2022-10-27

申请号：US17348118

申请日：2021-06-15

Applicant: Google LLC

Inventor： Thibault Doutre ,  Wei Han ,  Min Ma ,  Zhiyun Lu ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Arun Narayanan ,  Ananya Misra ,  Yu Zhang ,  Liangliang Cao

IPC: G10L15/06 ,  G06N3/04 ,  G10L15/18 ,  G10L15/08

Abstract: A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.

公开(公告)号：US20240371363A1

公开(公告)日：2024-11-07

申请号：US18772263

申请日：2024-07-15

Applicant: Google LLC

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

IPC: G10L15/06 ,  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：US12094453B2

公开(公告)日：2024-09-17

申请号：US17447285

申请日：2021-09-09

Applicant: Google LLC

Inventor： Jiahui Yu ,  Chung-cheng Chiu ,  Bo Li ,  Shuo-yiin Chang ,  Tara Sainath ,  Wei Han ,  Anmol Gulati ,  Yanzhang He ,  Arun Narayanan ,  Yonghui Wu ,  Ruoming Pang

IPC: G10L15/06 ,  G10L15/16 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30

CPC classification number: G10L15/063 ,  G10L15/16 ,  G10L15/22 ,  G10L15/30 ,  G10L15/187

Abstract: A computer-implemented method of training a streaming speech recognition model that includes receiving, as input to the streaming speech recognition model, a sequence of acoustic frames. The streaming speech recognition model is configured to learn an alignment probability between the sequence of acoustic frames and an output sequence of vocabulary tokens. The vocabulary tokens include a plurality of label tokens and a blank token. At each output step, the method includes determining a first probability of emitting one of the label tokens and determining a second probability of emitting the blank token. The method also includes generating the alignment probability at a sequence level based on the first probability and the second probability. The method also includes applying a tuning parameter to the alignment probability at the sequence level to maximize the first probability of emitting one of the label tokens.

公开(公告)号：US20240265923A1

公开(公告)日：2024-08-08

申请号：US18635974

申请日：2024-04-15

Applicant: GOOGLE LLC

Inventor： Asaf Aharoni ,  Arun Narayanan ,  Nir Shabat ,  Parisa Haghani ,  Galen Tsai Chuang ,  Yaniv Leviathan ,  Neeraj Gaur ,  Pedro J. Moreno Mengibar ,  Rohit Prakash Prabhavalkar ,  Zhongdi Qu ,  Austin Severn Waters ,  Tomer Amiaz ,  Michiel A.U. Bacchiani

IPC: G10L15/26 ,  G10L15/32 ,  H04M1/02 ,  H04M1/663 ,  H04M3/428 ,  H04M3/51

CPC classification number: G10L15/26 ,  G10L15/32 ,  H04M1/02 ,  H04M1/663 ,  H04M3/4286 ,  H04M3/5191

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

公开(公告)号：US12051434B2

公开(公告)日：2024-07-30

申请号：US17643825

申请日：2021-12-11

Applicant: Google LLC

Inventor： Turaj Zakizadeh Shabestary ,  Arun Narayanan

IPC: G10L21/0224 ,  G10L21/0208 ,  G10L21/0232

CPC classification number: G10L21/0224 ,  G10L21/0232 ,  G10L2021/02082

Abstract: A method for Short-Time Fourier Transform-based echo muting includes receiving a microphone signal including acoustic echo captured by a microphone and corresponding to audio content from an acoustic speaker, and receiving a reference signal including a sequence of frames representing the audio content. For each frame in a sequence of frames, the method includes processing, using an acoustic echo canceler configured to receive a respective frame as input to generate a respective output signal frame that cancels the acoustic echo from the respective frame, and determining, using a Double-talk Detector (DTD), based on the respective frame and the respective output signal frame, whether the respective frame includes a double-talk frame or an echo-only frame. For each respective frame that includes the echo-only frame, muting the respective output signal frame, and performing speech processing on the respective output signal frame for each respective frame that includes the double-talk frame.

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：US11990133B2

公开(公告)日：2024-05-21

申请号：US18219480

申请日：2023-07-07

Applicant: GOOGLE LLC

Inventor： Asaf Aharoni ,  Arun Narayanan ,  Nir Shabat ,  Parisa Haghani ,  Galen Tsai Chuang ,  Yaniv Leviathan ,  Neeraj Gaur ,  Pedro J. Moreno Mengibar ,  Rohit Prakash Prabhavalkar ,  Zhongdi Qu ,  Austin Severn Waters ,  Tomer Amiaz ,  Michiel A. U. Bacchiani

IPC: G10L15/26 ,  G10L15/32 ,  H04M1/02 ,  H04M1/663 ,  H04M3/428 ,  H04M3/51

CPC classification number: G10L15/26 ,  G10L15/32 ,  H04M1/02 ,  H04M1/663 ,  H04M3/4286 ,  H04M3/5191

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

公开(公告)号：US20240029716A1

公开(公告)日：2024-01-25

申请号：US18480827

申请日：2023-10-04

Applicant: Google LLC

Inventor： Thibault Doutre ,  Wei Han ,  Min Ma ,  Zhiyun Lu ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Arun Narayanan ,  Ananya Misra ,  Yu Zhang ,  Liangliang Cao

IPC: G10L15/06 ,  G10L15/08 ,  G10L15/18 ,  G06N3/045

CPC classification number: G10L15/063 ,  G10L15/083 ,  G10L15/18 ,  G06N3/045

Abstract: A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.

公开(公告)号：US20230352027A1

公开(公告)日：2023-11-02

申请号：US18219480

申请日：2023-07-07

Applicant: GOOGLE LLC

Inventor： Asaf Aharoni ,  Arun Narayanan ,  Nir Shabat ,  Parisa Haghani ,  Galen Tsai Chuang ,  Yaniv Leviathan ,  Neeraj Gaur ,  Pedro J. Moreno Mengibar ,  Rohit Prakash Prabhavalkar ,  Zhongdi Qu ,  Austin Severn Waters ,  Tomer Amiaz ,  Michiel A.U. Bacchiani

IPC: G10L15/26 ,  H04M3/428 ,  H04M1/663 ,  G10L15/32 ,  H04M3/51 ,  H04M1/02

CPC classification number: G10L15/26 ,  H04M3/4286 ,  H04M1/663 ,  G10L15/32 ,  H04M3/5191 ,  H04M1/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

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.