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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
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：US20240029720A1
公开(公告)日：2024-01-25
申请号：US18340175
申请日：2023-06-23
Applicant: Google LLC
Inventor： David Qiu , Tsendsuren Munkhdalai , Yangzhang He , Khe Chai Sim
CPC classification number: G10L15/16 , G10L15/02 , G10L15/22 , G10L15/063 , G10L15/19
Abstract: An automatic speech recognition (ASR) system that includes an ASR model, a neural associative memory (NAM) biasing model, and a confidence estimation model (CEM). The ASR model includes an audio encoder configured to encode a sequence of audio frames characterizing a spoken utterance into a sequence of higher-order feature representations, and a decoder configured to receive the sequence of higher-order feature representations and output a final speech recognition result. The NAM biasing model is configured to receive biasing contextual information and modify the sequence of higher-order feature representations based on the biasing contextual information to generate, as output, biasing context vectors. The CEM is configured to compute a confidence of the final speech recognition result output by the decoder. The CEM is connected to the biasing context vectors generated by the NAM biasing model.
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Patent Agency Ranking
公开(公告)号：US20230326461A1
公开(公告)日：2023-10-12
申请号：US18182925
申请日：2023-03-13
Applicant: Google LLC
Inventor： Shaojin Ding , Yangzhang He , Xin Wang , Weiran Wang , Trevor Strohman , Tara N. Sainath , Rohit Parkash Prabhavalkar , Robert David , Rina Panigrahy , Rami Botros , Qiao Liang , Ian Mcgraw , Ding Zhao , Dongseong Hwang
CPC classification number: G10L15/32 , G10L15/16 , G10L15/22 , G10L2015/223
Abstract: An automated speech recognition (ASR) model includes a first encoder, a first encoder, a second encoder, and a second 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 first decoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a first probability distribution over possible speech recognition hypotheses. The second encoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a second higher order feature representation for a corresponding first higher order feature frame. The second decoder receives, as input, the second higher order feature representation generated by the second encoder, and generates a second probability distribution over possible speech recognition hypotheses.
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公开(公告)号：US20240029718A1
公开(公告)日：2024-01-25
申请号：US18352211
申请日：2023-07-13
Applicant: Google LLC
Inventor： Antoine Jean Bruguier , David Qiu , Yangzhang He , Trevor Strohman
Abstract: A method includes processing, using a speech recognizer, a first portion of audio data to generate a first lattice, and generating a first partial transcription for an utterance based on the first lattice. The method includes processing, using the recognizer, a second portion of the data to generate, based on the first lattice, a second lattice representing a plurality of partial speech recognition hypotheses for the utterance and a plurality of corresponding speech recognition scores. For each particular partial speech recognition hypothesis, the method includes generating a corresponding re-ranked score based on the corresponding speech recognition score and whether the particular partial speech recognition hypothesis shares a prefix with the first partial transcription. The method includes generating a second partial transcription for the utterance by selecting the partial speech recognition hypothesis of the second plurality of partial speech recognition hypotheses having the highest corresponding re-ranked score.
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公开(公告)号：US11475880B2
公开(公告)日：2022-10-18
申请号：US16809403
申请日：2020-03-04
Applicant: Google LLC
Inventor： Shuo-yiin Chang , Rohit Prakash Prabhavalkar , Gabor Simko , Tara N. Sainath , Bo Li , Yangzhang He
Abstract: A method includes receiving audio data of an utterance and processing the audio data to obtain, as output from a speech recognition model configured to jointly perform speech decoding and endpointing of utterances: partial speech recognition results for the utterance; and an endpoint indication indicating when the utterance has ended. While processing the audio data, the method also includes detecting, based on the endpoint indication, the end of the utterance. In response to detecting the end of the utterance, the method also includes terminating the processing of any subsequent audio data received after the end of the utterance was detected.
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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：US20200335091A1
公开(公告)日：2020-10-22
申请号：US16809403
申请日：2020-03-04
Applicant: Google LLC
Inventor： Shuo-yiin Chang , Rohit Prakash Prabhavalkar , Gabor Simko , Tara N. Sainath , Bo Li , Yangzhang He
Abstract: A method includes receiving audio data of an utterance and processing the audio data to obtain, as output from a speech recognition model configured to jointly perform speech decoding and endpointing of utterances: partial speech recognition results for the utterance; and an endpoint indication indicating when the utterance has ended. While processing the audio data, the method also includes detecting, based on the endpoint indication, the end of the utterance. In response to detecting the end of the utterance, the method also includes terminating the processing of any subsequent audio data received after the end of the utterance was detected.
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