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公开(公告)号：US11580956B2
公开(公告)日：2023-02-14
申请号：US17204852
申请日：2021-03-17
Applicant: Google LLC
Inventor： Tara N. Sainath , Basi Garcia , David Rybach , Trevor Strohman , Ruoming Pang
Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.
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公开(公告)号：US20230298570A1
公开(公告)日：2023-09-21
申请号：US18187222
申请日：2023-03-21
Applicant: Google LLC
Inventor： Weiran Wang , Tongzhou Chen , Tara N. Sainath , Ehsan Variani , Rohit Prakash Prabhavalkar , Ronny Huang , Bhuvana Ramabhadran , Neeraj Gaur , Sepand Mavandadi , Charles Caleb Peyser , Trevor Strohman , Yangzhang He , David Rybach
CPC classification number: G10L15/063 , G10L15/19 , G10L15/22 , G10L15/16 , G10L15/02
Abstract: A method includes generating, using an audio encoder, a higher-order feature representation for each acoustic frame in a sequence of acoustic frames; generating, using a decoder, based on the higher-order feature representation, a plurality of speech recognition hypotheses, each hypotheses corresponding to a candidate transcription of an utterance and having an associated first likelihood score; generating, using an external language model, for each speech recognition hypothesis, a second likelihood score; determining, using a learnable fusion module, for each speech recognition hypothesis, a set of fusion weights based on the higher-order feature representation and the speech recognition hypothesis; and generating, using the learnable fusion module, for each speech recognition hypothesis, a third likelihood score based on the first likelihood score, the second likelihood score, and the set of fusion weights, the audio encoder and decoder trained using minimum additive error rate training in the presence of the external language model.
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公开(公告)号：US20220310072A1
公开(公告)日：2022-09-29
申请号：US17616129
申请日：2020-06-03
Applicant: GOOGLE LLC
Inventor： Tara N. Sainath , Ruoming Pang , David Rybach , Yanzhang He , Rohit Prabhavalkar , Wei Li , Mirkó Visontai , Qiao Liang , Trevor Strohman , Yonghui Wu , Ian C. McGraw , Chung-Cheng Chiu
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.
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公开(公告)号：US12027154B2
公开(公告)日：2024-07-02
申请号：US18167050
申请日：2023-02-09
Applicant: Google LLC
Inventor： Tara N. Sainath , Basilio Garcia Castillo , David Rybach , Trevor Strohman , Ruoming Pang
CPC classification number: G10L15/063 , G10L25/30 , G10L25/78
Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.
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公开(公告)号：US20230343328A1
公开(公告)日：2023-10-26
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
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.
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Patent Agency Ranking
公开(公告)号：US20230343332A1
公开(公告)日：2023-10-26
申请号：US18304064
申请日：2023-04-20
Applicant: Google LLC
Inventor： Ronny Huang , Shuo-yiin Chang , David Rybach , Rohit Prakash Prabhavalkar , Tara N. Sainath , Cyril Allauzen , Charles Caleb Peyser , Zhiyun Lu
CPC classification number: G10L15/197 , G10L15/02 , G10L15/04 , G10L15/063 , G10L15/22 , G10L25/93 , G10L2015/025 , G10L2025/932
Abstract: A joint segmenting and ASR model includes an encoder and decoder. The encoder configured to: receive a sequence of acoustic frames characterizing one or more utterances; and generate, at each output step, a higher order feature representation for a corresponding acoustic frame. The decoder configured to: receive the higher order feature representation and generate, at each output step: a probability distribution over possible speech recognition hypotheses, and an indication of whether the corresponding output step corresponds to an end of speech segment. The j oint segmenting and ASR model trained on a set of training samples, each training sample including: audio data characterizing a spoken utterance; and a corresponding transcription of the spoken utterance, the corresponding transcription having an end of speech segment ground truth token inserted into the corresponding transcription automatically based on a set of heuristic-based rules and exceptions applied to the training sample.
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公开(公告)号：US11715458B2
公开(公告)日：2023-08-01
申请号：US17316198
申请日：2021-05-10
Applicant: Google LLC
Inventor： Tara Sainath , Arun Narayanan , Rami Botros , Yanzhang He , Ehsan Variani , Cyril Allauzen , David Rybach , Ruoming Pang , Trevor Strohman
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.
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公开(公告)号：US11545142B2
公开(公告)日：2023-01-03
申请号：US16827937
申请日：2020-03-24
Applicant: Google LLC
Inventor： Ding Zhao , Bo Li , Ruoming Pang , Tara N. Sainath , David Rybach , Deepti Bhatia , Zelin Wu
IPC: G10L15/183 , G10L15/16 , G06N20/00 , G06K9/62 , G06N3/08
Abstract: A method includes receiving audio data encoding an utterance, processing, using a speech recognition model, the audio data to generate speech recognition scores for speech elements, and determining context scores for the speech elements based on context data indicating a context for the utterance. The method also includes executing, using the speech recognition scores and the context scores, a beam search decoding process to determine one or more candidate transcriptions for the utterance. The method also includes selecting a transcription for the utterance from the one or more candidate transcriptions.
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公开(公告)号：US20210350794A1
公开(公告)日：2021-11-11
申请号：US17204852
申请日：2021-03-17
Applicant: Google LLC
Inventor： Tara N. Sainath , Basi Garcia , David Rybach , Trevor Strohman , Ruoming Pang
Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.
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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
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.
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