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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：US20200026760A1
公开(公告)日：2020-01-23
申请号：US16518518
申请日：2019-07-22
Applicant: Google LLC
Inventor： Chung-Cheng Chiu , Colin Abraham Raffel
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhanced attention mechanisms. In some implementations, data indicating an input sequence is received. The data is processed using an encoder neural network to generate a sequence of encodings. A series of attention outputs is determined using one or more attender modules. Determining each attention output can include (i) selecting an encoding from the sequence of encodings and (ii) determining attention over a proper subset of the sequence of encodings, where the proper subset of encodings is determined based on a position of the selected encoding in the sequence of encodings. The selections of encodings are also monotonic through the sequence of encodings. An output sequence is generated by processing the attention outputs using a decoder neural network. An output is provided that indicates a language sequence determined from the output sequence.
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公开(公告)号：US10281885B1
公开(公告)日：2019-05-07
申请号：US15600699
申请日：2017-05-19
Applicant: Google LLC
Inventor： Chung-Cheng Chiu , Navdeep Jaitly , Ilya Sutskever , Yuping Luo
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. In one aspect, the system includes a recurrent neural network configured to, at each time step, receive am input for the time step and process the input to generate a progress score and a set of output scores; and a subsystem configured to, at each time step, generate the recurrent neural network input and provide the input to the recurrent neural network; determine, from the progress score, whether or not to emit a new output at the time step; and, in response to determining to emit a new output, select an output using the output scores and emit the selected output as the output at a next position in the output order.
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公开(公告)号：US12175202B2
公开(公告)日：2024-12-24
申请号：US17456958
申请日：2021-11-30
Applicant: Google LLC
Inventor： Chung-Cheng Chiu , Colin Abraham Raffel
Abstract: A method includes receiving a sequence of audio features characterizing an utterance and processing, using an encoder neural network, the sequence of audio features to generate a sequence of encodings. At each of a plurality of output steps, the method also includes determining a corresponding hard monotonic attention output to select an encoding from the sequence of encodings, identifying a proper subset of the sequence of encodings based on a position of the selected encoding in the sequence of encodings, and performing soft attention over the proper subset of the sequence of encodings to generate a context vector at the corresponding output step. The method also includes processing, using a decoder neural network, the context vector generated at the corresponding output step to predict a probability distribution over possible output labels at the corresponding output step.
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公开(公告)号：US20240362453A1
公开(公告)日：2024-10-31
申请号：US18766038
申请日：2024-07-08
Applicant: Google LLC
Inventor： Anmol Gulati , Weikeng Qin , Zhengdong Zhang , Ruoming Pang , Niki Parmar , Jiahui Yu , Wei Han , Chung-Cheng Chiu , Yu Zhang , Yonghui Wu , Shibo Wang
Abstract: Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.
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Patent Agency Ranking
公开(公告)号：US12079703B2
公开(公告)日：2024-09-03
申请号：US17139525
申请日：2020-12-31
Applicant: Google LLC
Inventor： Anmol Gulati , Ruoming Pang , Niki Parmar , Jiahui Yu , Wei Han , Chung-Cheng Chiu , Yu Zhang , Yonghui Wu , Shibo Wang , Weikeng Qin , Zhengdong Zhang
Abstract: Systems and methods can utilize a conformer model to process a data set for various data processing tasks, including, but not limited to, speech recognition, sound separation, protein synthesis determination, video or other image set analysis, and natural language processing. The conformer model can use feed-forward blocks, a self-attention block, and a convolution block to process data to learn global interactions and relative-offset-based local correlations of the input data.
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公开(公告)号：US11922932B2
公开(公告)日：2024-03-05
申请号：US18194586
申请日：2023-03-31
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 , G10L15/02 , G10L15/06 , G10L15/16 , 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 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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28.发明申请公开(公告)号：US20230038982A1
公开(公告)日：2023-02-09
申请号：US17644108
申请日：2021-12-14
Applicant: Google LLC
Inventor： Arun Narayanan , Tom O'malley , Quan Wang , Alex Park , James Walker , Nathan David Howard , Yanzhang He , Chung-Cheng Chiu
IPC: G10L21/0216 , G10L15/06 , H04R3/04 , G06N3/04
Abstract: A method for automatic speech recognition using joint acoustic echo cancellation, speech enhancement, and voice separation includes receiving, at a contextual frontend processing model, input speech features corresponding to a target utterance. The method also includes receiving, at the contextual frontend processing model, at least one of a reference audio signal, a contextual noise signal including noise prior to the target utterance, or a speaker embedding including voice characteristics of a target speaker that spoke the target utterance. The method further includes processing, using the contextual frontend processing model, the input speech features and the at least one of the reference audio signal, the contextual noise signal, or the speaker embedding vector to generate enhanced speech features.
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公开(公告)号：US20220122622A1
公开(公告)日：2022-04-21
申请号：US17237021
申请日：2021-04-21
Applicant: Google LLC
Inventor： Arun Narayanan , Tara Sainath , Chung-Cheng Chiu , Ruoming Pang , Rohit Prabhavalkar , Jiahui Yu , Ehsan Variani , Trevor Strohman
Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a 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 second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.
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公开(公告)号：US20220083743A1
公开(公告)日：2022-03-17
申请号：US17456958
申请日：2021-11-30
Applicant: Google LLC
Inventor： Chung-Cheng Chiu , Colin Abraham Raffel
Abstract: A method includes receiving a sequence of audio features characterizing an utterance and processing, using an encoder neural network, the sequence of audio features to generate a sequence of encodings. At each of a plurality of output steps, the method also includes determining a corresponding hard monotonic attention output to select an encoding from the sequence of encodings, identifying a proper subset of the sequence of encodings based on a position of the selected encoding in the sequence of encodings, and performing soft attention over the proper subset of the sequence of encodings to generate a context vector at the corresponding output step. The method also includes processing, using a decoder neural network, the context vector generated at the corresponding output step to predict a probability distribution over possible output labels at the corresponding output step.
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