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公开(公告)号：US11145293B2
公开(公告)日：2021-10-12
申请号：US16516390
申请日：2019-07-19
Applicant: Google LLC
Inventor： Rohit Prakash Prabhavalkar , Zhifeng Chen , Bo Li , Chung-Cheng Chiu , Kanury Kanishka Rao , Yonghui Wu , Ron J. Weiss , Navdeep Jaitly , Michiel A. U. Bacchiani , Tara N. Sainath , Jan Kazimierz Chorowski , Anjuli Patricia Kannan , Ekaterina Gonina , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer-readable media, for performing speech recognition using sequence-to-sequence models. An automated speech recognition (ASR) system receives audio data for an utterance and provides features indicative of acoustic characteristics of the utterance as input to an encoder. The system processes an output of the encoder using an attender to generate a context vector and generates speech recognition scores using the context vector and a decoder trained using a training process that selects at least one input to the decoder with a predetermined probability. An input to the decoder during training is selected between input data based on a known value for an element in a training example, and input data based on an output of the decoder for the element in the training example. A transcription is generated for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.
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公开(公告)号：US12175963B2
公开(公告)日：2024-12-24
申请号：US18525475
申请日：2023-11-30
Applicant: Google LLC
Inventor： Ye Jia , Zhifeng Chen , Yonghui Wu , Jonathan Shen , Ruoming Pang , Ron J. Weiss , Ignacio Lopez Moreno , Fei Ren , Yu Zhang , Quan Wang , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.
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公开(公告)号：US11646019B2
公开(公告)日：2023-05-09
申请号：US17443557
申请日：2021-07-27
Applicant: Google LLC
Inventor： Rohit Prakash Prabhavalkar , Tara N. Sainath , Yonghui Wu , Patrick An Phu Nguyen , Zhifeng Chen , Chung-Cheng Chiu , Anjuli Patricia Kannan
IPC: G10L15/197 , G10L15/16 , G10L15/06 , G10L15/02 , G10L15/22
CPC classification number: G10L15/197 , G10L15/02 , G10L15/063 , G10L15/16 , G10L15/22 , G10L2015/025
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses N-best lists of decoded hypotheses, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.
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公开(公告)号：US20220351713A1
公开(公告)日：2022-11-03
申请号：US17813361
申请日：2022-07-19
Applicant: Google LLC
Inventor： Ye Jia , Zhifeng Chen , Yonghui Wu , Jonathan Shen , Ruoming Pang , Ron J. Weiss , Ignacio Lopez Moreno , Fei Ren , Yu Zhang , Quan Wang , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.
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公开(公告)号：US20200043483A1
公开(公告)日：2020-02-06
申请号：US16529252
申请日：2019-08-01
Applicant: Google LLC
Inventor： Rohit Prakash Prabhavalkar , Tara N. Sainath , Yonghui Wu , Patrick An Phu Nguyen , Zhifeng Chen , Chung-Cheng Chiu , Anjuli Patricia Kannan
IPC: G10L15/197 , G10L15/16 , G10L15/22 , G10L15/06 , G10L15/02
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses N-best lists of decoded hypotheses, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.
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Patent Agency Ranking
公开(公告)号：US20200027444A1
公开(公告)日：2020-01-23
申请号：US16516390
申请日：2019-07-19
Applicant: Google LLC
Inventor： Rohit Prakash Prabhavalkar , Zhifeng Chen , Bo Li , Chung-Cheng Chiu , Kanury Kanishka Rao , Yonghui Wu , Ron J. Weiss , Navdeep Jaitly , Michiel A.U. Bacchiani , Tara N. Sainath , Jan Kazimierz Chorowski , Anjuli Patricia Kannan , Ekaterina Gonina , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer-readable media, for performing speech recognition using sequence-to-sequence models. An automated speech recognition (ASR) system receives audio data for an utterance and provides features indicative of acoustic characteristics of the utterance as input to an encoder. The system processes an output of the encoder using an attender to generate a context vector and generates speech recognition scores using the context vector and a decoder trained using a training process that selects at least one input to the decoder with a predetermined probability. An input to the decoder during training is selected between input data based on a known value for an element in a training example, and input data based on an output of the decoder for the element in the training example. A transcription is generated for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.
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公开(公告)号：US20230237995A1
公开(公告)日：2023-07-27
申请号：US18194586
申请日：2023-03-31
Applicant: Google LLC
Inventor： Rohit Prakash Prabhavalkar , Tara N. Sainath , Younghui Wu , Patrick An Phu Nguyen , Zhifeng Chen , Chung-Cheng Chiu , Anjuli Kannan
IPC: G10L15/197 , G10L15/16 , G10L15/06 , G10L15/02 , G10L15/22
CPC classification number: G10L15/197 , G10L15/16 , G10L15/063 , G10L15/02 , G10L15/22 , G10L2015/025
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses a set of speech recognition hypothesis samples, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.
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公开(公告)号：US10276163B1
公开(公告)日：2019-04-30
申请号：US15196893
申请日：2016-06-29
Applicant: Google LLC
Inventor： Xin Lei , Patrick An Phu Nguyen
Abstract: Audio data that encodes an utterance of a user is received. It is determined that the user has been classified as a novice user of a speech recognizer. A speech recognizer setting is selected that is used by the speech recognizer in generating a transcription of the utterance. The selected speech recognizer setting is different than a default speech recognizer setting that is used by the speech recognizer in generating transcriptions of utterances of users that are not classified as novice users. The selected speech recognizer setting results in increased speech recognition accuracy in comparison with the default setting. A transcription of the utterance is obtained that is generated by the speech recognizer using the selected setting.
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公开(公告)号：US12254865B2
公开(公告)日：2025-03-18
申请号：US18418246
申请日：2024-01-20
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：US20240161732A1
公开(公告)日：2024-05-16
申请号：US18418246
申请日：2024-01-20
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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