-
21.发明公开公开(公告)号：US20240169981A1
公开(公告)日：2024-05-23
申请号：US18512110
申请日：2023-11-17
Applicant: Google LLC
Inventor： Wenqian Ronny Huang , Shuo-yiin Chang , Tara N. Sainath , Yanzhang He
IPC: G10L15/197 , G10L15/02 , G10L15/05 , G10L15/06 , G10L15/16
CPC classification number: G10L15/197 , G10L15/02 , G10L15/05 , G10L15/063 , G10L15/16 , G10L2015/025 , G10L15/22
Abstract: A unified end-to-end segmenter and two-pass automatic speech recognition (ASR) model includes a first encoder, a first decoder, a second encoder, and a second decoder. The first encoder is configured to receive a sequence of acoustic frames and generate a first higher order feature representation. The first decoder is configured to receive the first higher order feature representation and generate, at each of a plurality of output steps, a first probability distribution and an indication of whether the output step corresponds to an end of speech segment, and emit an end of speech timestamp. The second encoder is configured to receive the first higher order feature representation and the end of speech timestamp, and generate a second higher order feature representation. The second decoder is configured to receive the second higher order feature representation and generate a second probability distribution.
-
公开(公告)号：US20230186901A1
公开(公告)日：2023-06-15
申请号：US18167454
申请日：2023-02-10
Applicant: Google LLC
Inventor： Tara N. Sainath , Ruoming Pang , Ron Weiss , Yanzhang He , Chung-Cheng Chiu , Trevor Strohman
IPC: G10L15/06 , G06N3/08 , G10L15/16 , G10L15/197
CPC classification number: G10L15/063 , G06N3/08 , G10L15/16 , G10L15/197 , G10L2015/0635
Abstract: A method includes receiving a training example for a listen-attend-spell (LAS) decoder of a two-pass streaming neural network model and determining whether the training example corresponds to a supervised audio-text pair or an unpaired text sequence. When the training example corresponds to an unpaired text sequence, the method also includes determining a cross entropy loss based on a log probability associated with a context vector of the training example. The method also includes updating the LAS decoder and the context vector based on the determined cross entropy loss.
-
公开(公告)号：US20220238101A1
公开(公告)日：2022-07-28
申请号：US17616135
申请日：2020-12-03
Applicant: GOOGLE LLC
Inventor： Tara N. Sainath , Yanzhang He , Bo Li , Arun Narayanan , Ruoming Pang , Antoine Jean Bruguier , Shuo-yiin Chang , Wei Li
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.
-
公开(公告)号：US20220122586A1
公开(公告)日：2022-04-21
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
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：US11295739B2
公开(公告)日：2022-04-05
申请号：US16527487
申请日：2019-07-31
Applicant: Google LLC
Inventor： Wei Li , Rohit Prakash Prabhavalkar , Kanury Kanishka Rao , Yanzhang He , Ian C. McGraw , Anton Bakhtin
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.
-
Patent Agency Ranking
公开(公告)号：US20210225362A1
公开(公告)日：2021-07-22
申请号：US17155010
申请日：2021-01-21
Applicant: Google LLC
Inventor： Tara N. Sainath , Ruorning Pang , Ron Weiss , Yanzhang He , Chung-Cheng Chiu , Trevor Strohman
IPC: G10L15/06 , G10L15/16 , G10L15/197 , G06N3/08
Abstract: A method includes receiving a training example for a listen-attend-spell (LAS) decoder of a two-pass streaming neural network model and determining whether the training example corresponds to a supervised audio-text pair or an unpaired text sequence. When the training example corresponds to an unpaired text sequence, the method also includes determining a cross entropy loss based on a log probability associated with a context vector of the training example. The method also includes updating the LAS decoder and the context vector based on the determined cross entropy loss.
-
公开(公告)号：US20200066271A1
公开(公告)日：2020-02-27
申请号：US16527487
申请日：2019-07-31
Applicant: Google LLC
Inventor： Wei Li , Rohit Prakash Prabhavalkar , Kanury Kanishka Rao , Yanzhang He , Ian C. McGraw , Anton Bakhtin
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.
-
公开(公告)号：US20250095634A1
公开(公告)日：2025-03-20
申请号：US18965193
申请日：2024-12-02
Applicant: Google LLC
Inventor： Bo Li , Tara N. Sainath , Ruoming Pang , Shuo-yiin Chang , Qiumin Xu , Trevor Strohman , Vince Chen , Qiao Liang , Heguang Liu , Yanzhang He , Parisa Haghani , Sameer Bidichandani
Abstract: A method includes receiving a sequence of acoustic frames characterizing one or more utterances as input to a multilingual automated speech recognition (ASR) model. The method also includes generating a higher order feature representation for a corresponding acoustic frame. The method also includes generating a hidden representation based on a sequence of non-blank symbols output by a final softmax layer. The method also includes generating a probability distribution over possible speech recognition hypotheses based on the hidden representation generated by the prediction network at each of the plurality of output steps and the higher order feature representation generated by the encoder at each of the plurality of output steps. The method also includes predicting an end of utterance (EOU) token at an end of each utterance. The method also includes classifying each acoustic frame as either speech, initial silence, intermediate silence, or final silence.
-
公开(公告)号：US12073824B2
公开(公告)日：2024-08-27
申请号：US17616135
申请日：2020-12-03
Applicant: GOOGLE LLC
Inventor： Tara N. Sainath , Yanzhang He , Bo Li , Arun Narayanan , Ruoming Pang , Antoine Jean Bruguier , Shuo-Yiin Chang , Wei Li
CPC classification number: G10L15/16 , G06N3/08 , G10L15/05 , G10L15/063 , G10L15/22 , G10L2015/0635
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.
-
公开(公告)号：US20240221750A1
公开(公告)日：2024-07-04
申请号：US18610233
申请日：2024-03-19
Applicant: Google LLC
Inventor： Wei Li , Rohit Prakash Prabhavalkar , Kanury Kanishka Rao , Yanzhang He , Ian C. McGraw , Anton Bakhtin
CPC classification number: G10L15/22 , G10L15/02 , G10L15/063 , G10L15/18 , G10L19/00 , G10L2015/025 , G10L2015/088 , G10L15/142 , G10L2015/223
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.
-
-
-
-
-
-
-
-
-
Search Results
39
records
(took 0.018 seconds)
