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公开(公告)号：US20240221627A1
公开(公告)日：2024-07-04
申请号：US18544051
申请日：2023-12-18
Applicant: GOOGLE LLC
Inventor： Edwin Lyle Hudson , Bo Li
IPC: G09G3/32 , G11C11/412
CPC classification number: G09G3/32 , G11C11/412 , G09G2300/0842 , G09G2310/0297
Abstract: A backplane operative to drive an array of emissive pixel elements is disclosed. A plurality of pixel drive circuits form part of an array of emissive elements. The plurality of pixel drive circuits are disposed to form a plurality of rows and a plurality of columns. The plurality of pixel drive circuits are organized into sets of pixel drive circuits, and each set comprises at least one pixel drive circuit.
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公开(公告)号：US20240185841A1
公开(公告)日：2024-06-06
申请号：US18490808
申请日：2023-10-20
Applicant: Google LLC
Inventor： Bo Li , Yu Zhang , Nanxin Chen , Rohit Prakash Prabhavalkar , Chao-Han Huck Yang , Tara N. Sainath , Trevor Strohman
IPC: G10L15/065 , G10L15/00
CPC classification number: G10L15/065 , G10L15/005
Abstract: A method includes obtaining an ASR model trained to recognize speech in a first language and receiving transcribed training utterances in a second language. The method also includes integrating the ASR model with an input reprogramming module and a latent reprogramming module. The method also includes adapting the ASR model to learn how to recognize speech in the second language by training the input reprogramming module and the latent reprogramming module while parameters of the ASR model are frozen.
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公开(公告)号：US11900915B2
公开(公告)日：2024-02-13
申请号：US17572238
申请日：2022-01-10
Applicant: Google LLC
Inventor： Zhifeng Chen , Bo Li , Eugene Weinstein , Yonghui Wu , Pedro J. Moreno Mengibar , Ron J. Weiss , Khe Chai Sim , Tara N. Sainath , Patrick An Phu Nguyen
CPC classification number: G10L15/005 , G10L15/07 , G10L15/16 , G10L2015/0631
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.
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Patent Agency Ranking
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：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.
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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.
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公开(公告)号：US11238845B2
公开(公告)日：2022-02-01
申请号：US16684483
申请日：2019-11-14
Applicant: GOOGLE LLC
Inventor： Zhifeng Chen , Bo Li , Eugene Weinstein , Yonghui Wu , Pedro J. Moreno Mengibar , Ron J. Weiss , Khe Chai Sim , Tara N. Sainath , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.
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公开(公告)号：US20200160836A1
公开(公告)日：2020-05-21
申请号：US16684483
申请日：2019-11-14
Applicant: GOOGLE LLC
Inventor： Zhifeng Chen , Bo Li , Eugene Weinstein , Yonghui Wu , Pedro J. Moreno Mengibar , Ron J. Weiss , Khe Chai Sim , Tara N. Sainath , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.
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