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公开(公告)号：US20220319498A1
公开(公告)日：2022-10-06
申请号：US17221220
申请日：2021-04-02
Applicant: Google LLC
Inventor： Joseph Caroselli, JR. , Yiteng Huang , Arun Narayanan
IPC: G10L15/08 , G10L21/0216 , G10L15/05 , G06N20/00
Abstract: Implementations disclosed herein are directed to initializing and utilizing a beamformer in processing of audio data received at a computing device. The computing device can: receive audio data that captures a spoken utterance of a user, determine that a first audio data segment of the audio data includes one or more particular words or phrases; obtain a preceding audio data segment that precedes the first audio data segment; estimate a spatial correlation matrix based on the first audio data segment and based on the preceding audio data segment; initialize the beamformer based on the estimated spatial correlation matrix; and cause the initialized beamformer to be utilized in processing of at least a second audio data segment of the audio data. Additionally, or alternatively, the computing device can transmit the spatial correlation matrix to server(s), and the server(s) can transmit the initialized beamformer back to the computing device.
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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：US20190259409A1
公开(公告)日：2019-08-22
申请号：US16278830
申请日：2019-02-19
Applicant: Google LLC
Inventor： Ehsan Variani , Kevin William Wilson , Ron J. Weiss , Tara N. Sainath , Arun Narayanan
IPC: G10L25/30 , G10L21/028 , G10L19/008 , G10L15/20 , G10L15/16 , G10L21/0388
Abstract: This specification describes computer-implemented methods and systems. One method includes receiving, by a neural network of a speech recognition system, first data representing a first raw audio signal and second data representing a second raw audio signal. The first raw audio signal and the second raw audio signal describe audio occurring at a same period of time. The method further includes generating, by a spatial filtering layer of the neural network, a spatial filtered output using the first data and the second data, and generating, by a spectral filtering layer of the neural network, a spectral filtered output using the spatial filtered output. Generating the spectral filtered output comprises processing frequency-domain data representing the spatial filtered output. The method still further includes processing, by one or more additional layers of the neural network, the spectral filtered output to predict sub-word units encoded in both the first raw audio signal and the second raw audio signal.
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15.发明申请公开(公告)号：US20250029624A1
公开(公告)日：2025-01-23
申请号：US18906761
申请日：2024-10-04
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 , G06N3/04 , G10L15/06 , G10L21/0208 , H04R3/04
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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Patent Agency Ranking
公开(公告)号：US20240428786A1
公开(公告)日：2024-12-26
申请号：US18826655
申请日：2024-09-06
Applicant: Google LLC
Inventor： Ke Hu , Tara N. Sainath , Arun Narayanan , Ruoming Pang , Trevor Strohman
IPC: G10L15/197 , G06F40/126 , G10L15/02 , G10L15/06 , G10L15/08 , G10L15/22
Abstract: A method includes receiving a sequence of acoustic frames and generating, by a first encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by a first pass transducer decoder, a first pass speech recognition hypothesis for a corresponding first higher order feature representation and generating, by a text encoder, a text encoding for a corresponding first pass speech recognition hypothesis. The method also includes generating, by a second encoder, a second higher order feature representation for a corresponding first higher order feature representation. The method also includes generating, by a second pass transducer decoder, a second pass speech recognition hypothesis using a corresponding second higher order feature representation and a corresponding text encoding.
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17.发明公开公开(公告)号：US20230298612A1
公开(公告)日：2023-09-21
申请号：US18171411
申请日：2023-02-20
Applicant: Google LLC
Inventor： Joseph Caroselli , Arun Narayanan , Tom O'malley
CPC classification number: G10L21/0232 , G10L25/30 , H04S3/008 , G10L15/22 , G10L15/063 , G10L15/16 , G10L25/18 , H04S2400/01 , G10L2021/02082
Abstract: A multichannel neural frontend speech enhancement model for speech recognition includes a speech cleaner, a stack of self-attention blocks each having a multi-headed self attention mechanism, and a masking layer. The speech cleaner receives, as input, a multichannel noisy input signal and a multichannel contextual noise signal, and generates, as output, a single channel cleaned input signal. The stack of self-attention blocks receives, as input, at an initial block of the stack of self-attention blocks, a stacked input including the single channel cleaned input signal and a single channel noisy input signal, and generates, as output, from a final block of the stack of self-attention blocks, an un-masked output. The masking layer receives, as input, the single channel noisy input signal and the un-masked output, and generates, as output, enhanced input speech features corresponding to a target utterance.
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18.发明公开公开(公告)号：US20230298609A1
公开(公告)日：2023-09-21
申请号：US18171368
申请日：2023-02-19
Applicant: Google LLC
Inventor： Tom O'Malley , Quan Wang , Arun Narayanan
IPC: G10L21/0208 , G10L15/06
CPC classification number: G10L21/0208 , G10L15/063 , G10L2021/02082
Abstract: A method for training a generalized automatic speech recognition model for joint acoustic echo cancellation, speech enhancement, and voice separation includes receiving a plurality of training utterances paired with corresponding training contextual signals. The training contextual signals include a training contextual noise signal including noise prior to the corresponding training utterance, a training reference audio signal, and a training speaker vector including voice characteristics of a target speaker that spoke the corresponding training utterance. The operations also include training, using a contextual signal dropout strategy, a contextual frontend processing model on the training utterances to learn how to predict enhanced speech features. Here, the contextual signal dropout strategy uses a predetermined probability to drop out each of the training contextual signals during training of the contextual frontend processing model.
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公开(公告)号：US20230038343A1
公开(公告)日：2023-02-09
申请号：US17964141
申请日：2022-10-12
Applicant: GOOGLE LLC
Inventor： Asaf Aharoni , Arun Narayanan , Nir Shabat , Parisa Haghani , Galen Tsai Chuang , Yaniv Leviathan , Neeraj Gaur , Pedro J. Moreno Mengibar , Rohit Prakash Prabhavalkar , Zhongdi Qu , Austin Severn Waters , Tomer Amiaz , Michiel A.U. Bacchiani
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.
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公开(公告)号：US11495233B2
公开(公告)日：2022-11-08
申请号：US17505913
申请日：2021-10-20
Applicant: GOOGLE LLC
Inventor： Asaf Aharoni , Arun Narayanan , Nir Shabat , Parisa Haghani , Galen Tsai Chuang , Yaniv Leviathan , Neeraj Gaur , Pedro J. Moreno Mengibar , Rohit Prakash Prabhavalkar , Zhongdi Qu , Austin Severn Waters , Tomer Amiaz , Michiel A.U. Bacchiani
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.
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