公开(公告)号：US20190332919A1

公开(公告)日：2019-10-31

申请号：US16504924

申请日：2019-07-08

Applicant: Google LLC

Inventor： Ron J. Weiss ,  Thang Minh Luong ,  Peter J. Liu ,  Colin Abraham Raffel ,  Douglas Eck

IPC: G06N3/04 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence including a respective output at each of multiple output time steps from respective encoded representations of inputs in an input sequence. The method includes, for each output time step, starting from the position, in the input order, of the encoded representation that was selected as a preceding context vector at a preceding output time step, traversing the encoded representations until an encoded representation is selected as a current context vector at the output time step. A decoder neural network processes the current context vector and a preceding output at the preceding output time step to generate a respective output score for each possible output and to update the hidden state of the decoder recurrent neural network. An output is selected for the output time step using the output scores.

公开(公告)号：US20240420686A1

公开(公告)日：2024-12-19

申请号：US18815200

申请日：2024-08-26

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

IPC: G10L15/16 ,  G06N3/08 ,  G10L15/02 ,  G10L15/06 ,  G10L15/22 ,  G10L15/26 ,  G10L25/30

Abstract: A method for performing speech recognition using sequence-to-sequence models includes receiving audio data for an utterance and providing features indicative of acoustic characteristics of the utterance as input to an encoder. The method also includes processing an output of the encoder using an attender to generate a context vector, generating speech recognition scores using the context vector and a decoder trained using a training process, and generating a transcription for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.

公开(公告)号：US12087273B2

公开(公告)日：2024-09-10

申请号：US18161217

申请日：2023-01-30

Applicant: Google LLC

Inventor： Yu Zhang ,  Ron J. Weiss ,  Byungha Chun ,  Yonghui Wu ,  Zhifeng Chen ,  Russell John Wyatt Skerry-Ryan ,  Ye Jia ,  Andrew M. Rosenberg ,  Bhuvana Ramabhadran

IPC: G10L21/00 ,  G10L13/00 ,  G10L13/047

CPC classification number: G10L13/047

Abstract: A method includes receiving an input text sequence to be synthesized into speech in a first language and obtaining a speaker embedding, the speaker embedding specifying specific voice characteristics of a target speaker for synthesizing the input text sequence into speech that clones a voice of the target speaker. The target speaker includes a native speaker of a second language different than the first language. The method also includes generating, using a text-to-speech (TTS) model, an output audio feature representation of the input text by processing the input text sequence and the speaker embedding. The output audio feature representation includes the voice characteristics of the target speaker specified by the speaker embedding.

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

IPC: G10L15/00 ,  G10L15/16 ,  G10L15/07 ,  G10L15/06

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.

公开(公告)号：US11862142B2

公开(公告)日：2024-01-02

申请号：US17391799

申请日：2021-08-02

Applicant: Google LLC

Inventor： Samuel Bengio ,  Yuxuan Wang ,  Zongheng Yang ,  Zhifeng Chen ,  Yonghui Wu ,  Ioannis Agiomyrgiannakis ,  Ron J. Weiss ,  Navdeep Jaitly ,  Ryan M. Rifkin ,  Robert Andrew James Clark ,  Quoc V. Le ,  Russell J. Ryan ,  Ying Xiao

IPC: G10L13/06 ,  G10L13/08 ,  G06N3/08 ,  G10L25/18 ,  G10L25/30 ,  G10L13/04 ,  G06N3/084 ,  G10L15/16 ,  G06N3/045

CPC classification number: G10L13/08 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084 ,  G10L13/04 ,  G10L15/16 ,  G10L25/18 ,  G10L25/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.

公开(公告)号：US20230325658A1

公开(公告)日：2023-10-12

申请号：US18010426

申请日：2021-09-02

Applicant: Google LLC

Inventor： Nanxin Chen ,  Byungha Chun ,  William Chan ,  Ron J. Weiss ,  Mohammad Norouzi ,  Yu Zhang ,  Yonghui Wu

IPC: G06V10/82 ,  G06N3/08 ,  G10L13/02 ,  G10L25/18 ,  G10L25/30 ,  G06V10/764 ,  G06V10/26

CPC classification number: G06N3/08 ,  G06V10/26 ,  G06V10/764 ,  G06V10/82 ,  G10L13/02 ,  G10L25/18 ,  G10L25/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating outputs conditioned on network inputs using neural networks. In one aspect, a method comprises obtaining the network input; initializing a current network output; and generating the final network output by updating the current network output at each of a plurality of iterations, wherein each iteration corresponds to a respective noise level, and wherein the updating comprises, at each iteration: processing a model input for the iteration comprising (i) the current network output and (ii) the network input using a noise estimation neural network that is configured to process the model input to generate a noise output, wherein the noise output comprises a respective noise estimate for each value in the current network output; and updating the current network output using the noise estimate and the noise level for the iteration.

公开(公告)号：US11783849B2

公开(公告)日：2023-10-10

申请号：US17303822

申请日：2021-06-08

Applicant: Google LLC

Inventor： Ehsan Variani ,  Kevin William Wilson ,  Ron J. Weiss ,  Tara N. Sainath ,  Arun Narayanan

IPC: G10L15/16 ,  G10L25/30 ,  G10L21/028 ,  G10L21/0388 ,  G10L19/008 ,  G10L15/20 ,  G10L21/0208 ,  G10L21/0216

CPC classification number: G10L25/30 ,  G10L15/16 ,  G10L15/20 ,  G10L19/008 ,  G10L21/028 ,  G10L21/0388 ,  G10L2021/02087 ,  G10L2021/02166

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.

公开(公告)号：US11580952B2

公开(公告)日：2023-02-14

申请号：US16855042

申请日：2020-04-22

Applicant: Google LLC

Inventor： Yu Zhang ,  Ron J. Weiss ,  Byungha Chun ,  Yonghui Wu ,  Zhifeng Chen ,  Russell John Wyatt Skerry-Ryan ,  Ye Jia ,  Andrew M. Rosenberg ,  Bhuvana Ramabhadran

IPC: G10L13/00 ,  G10L13/047

Abstract: A method includes receiving an input text sequence to be synthesized into speech in a first language and obtaining a speaker embedding, the speaker embedding specifying specific voice characteristics of a target speaker for synthesizing the input text sequence into speech that clones a voice of the target speaker. The target speaker includes a native speaker of a second language different than the first language. The method also includes generating, using a text-to-speech (TTS) model, an output audio feature representation of the input text by processing the input text sequence and the speaker embedding. The output audio feature representation includes the voice characteristics of the target speaker specified by the speaker embedding.

公开(公告)号：US20220122582A1

公开(公告)日：2022-04-21

申请号：US17327076

申请日：2021-05-21

Applicant: Google LLC

Inventor： Isaac Elias ,  Jonathan Shen ,  Yu Zhang ,  Ye Jia ,  Ron J. Weiss ,  Yonghui Wu ,  Byungha Chun

IPC: G10L13/08 ,  G10L21/10 ,  G06F40/126 ,  G06N3/08

Abstract: A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

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

IPC: G10L15/00 ,  G10L15/07 ,  G10L15/16 ,  G10L15/06

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.