公开(公告)号：US20240112027A1

公开(公告)日：2024-04-04

申请号：US18477546

申请日：2023-09-28

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Yanping Huang ,  Yifeng Lu ,  Andrew M. Dai ,  Siamak Shakeri ,  Zhifeng Chen ,  James Laudon ,  Quoc V. Le ,  Da Huang ,  Nan Du ,  David Richard So ,  Daiyi Peng ,  Yingwei Cui ,  Jeffrey Adgate Dean ,  Chang Lan

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

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：US20230222318A1

公开(公告)日：2023-07-13

申请号：US18009841

申请日：2021-06-30

Applicant: Google LLC

Inventor： Dmitry Lepikhin ,  Yanping Huang ,  Orhan Firat ,  Maxim Krikun ,  Dehao Chen ,  Noam M. Shazeer ,  HyoukJoong Lee ,  Yuanzhong Xu ,  Zhifeng Chen

IPC: G06N3/042 ,  G06N3/098

CPC classification number: G06N3/042 ,  G06N3/098

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing machine learning task on a network input to generate a network output. In one aspect, one of the systems includes an attention neural network configured to perform the machine learning task, the attention neural network including one or more attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer. Some or all of the attention layers have a feed-forward sub-layer that applies conditional computation to the inputs to the sub-layer.

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：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.

公开(公告)号：US20210358491A1

公开(公告)日：2021-11-18

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

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.

公开(公告)号：US11107463B2

公开(公告)日：2021-08-31

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 ,  G10L15/06 ,  G10L15/02 ,  G10L15/22

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.

公开(公告)号：US20210217404A1

公开(公告)日：2021-07-15

申请号：US17055951

申请日：2019-05-17

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 Nguyen

IPC: G10L13/04 ,  G10L19/00 ,  G10L17/04

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.

公开(公告)号：US10713593B2

公开(公告)日：2020-07-14

申请号：US15394708

申请日：2016-12-29

Applicant: Google LLC

Inventor： Zhifeng Chen ,  Michael Schuster ,  Melvin Jose Johnson Premkumar ,  Yonghui Wu ,  Quoc V. Le ,  Maxim Krikun ,  Thorsten Brants

IPC: G06N20/00 ,  G06N3/04 ,  G06N3/063 ,  G06F40/44 ,  G06F40/47

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input.