公开(公告)号：US20230037085A1

公开(公告)日：2023-02-02

申请号：US17788183

申请日：2021-01-07

Applicant: GOOGLE LLC

Inventor： Fadi Biadsy ,  Johan Schalkwyk ,  Jason Pelecanos

IPC: G10L15/18 ,  G10L15/22

Abstract: Implementations disclosed herein are directed to techniques for selectively enabling and/or disabling non-transient storage of one or more instances of assistant interaction data for turn(s) of a dialog between a user and an automated assistant. Implementations are additionally or alternatively directed to techniques for retroactive wiping of non-transiently stored assistant interaction data from previous assistant interaction(s).

公开(公告)号：US20220414542A1

公开(公告)日：2022-12-29

申请号：US17851712

申请日：2022-06-28

Applicant: Google LLC

Inventor： Fadi Biadsy ,  Katrin Ruth Sarah Tomanek

IPC: G06N20/00 ,  G06N5/02

Abstract: The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to on-the-fly feeding of personalized, domain-specific, context-specific, and/or task-specific submodels as input to an existing base model which has already been loaded into a memory (e.g., loaded into an existing session associated with execution of a machine learning library).

公开(公告)号：US20220068257A1

公开(公告)日：2022-03-03

申请号：US17008278

申请日：2020-08-31

Applicant: Google LLC

Inventor： Fadi Biadsy ,  Liyang Jiang ,  Pedro J. Moreno Mengibar ,  Andrew Rosenberg

IPC: G10L13/047 ,  G10L13/08 ,  G10L15/16 ,  G10L15/22

Abstract: A method for training a speech conversion model personalized for a target speaker with atypical speech includes obtaining a plurality of transcriptions in a set of spoken training utterances and obtaining a plurality of unspoken training text utterances. Each spoken training utterance is spoken by a target speaker associated with atypical speech and includes a corresponding transcription paired with a corresponding non-synthetic speech representation. The method also includes adapting, using the set of spoken training utterances, a text-to-speech (TTS) model to synthesize speech in a voice of the target speaker and that captures the atypical speech. For each unspoken training text utterance, the method also includes generating, as output from the adapted TTS model, a synthetic speech representation that includes the voice of the target speaker and that captures the atypical speech. The method also includes training the speech conversion model based on the synthetic speech representations.

公开(公告)号：US20210209315A1

公开(公告)日：2021-07-08

申请号：US17056554

申请日：2020-03-07

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Melvin Johnson ,  Fadi Biadsy ,  Ron Weiss ,  Wolfgang Macherey

IPC: G06F40/47 ,  G06F40/58

Abstract: The present disclosure provides systems and methods that train and use machine-learned models such as, for example, sequence-to-sequence models, to perform direct and text-free speech-to-speech translation. In particular, aspects of the present disclosure provide an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation.

公开(公告)号：US20210020170A1

公开(公告)日：2021-01-21

申请号：US17060347

申请日：2020-10-01

Applicant: Google LLC

Inventor： Fadi Biadsy ,  Diamantino Antionio Caseiro

IPC: G10L15/197 ,  G10L15/02 ,  G10L15/32 ,  G10L15/18

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for language models using domain-specific model components. In some implementations, context data for an utterance is obtained. A domain-specific model component is selected from among multiple domain-specific model components of a language model based on the non-linguistic context of the utterance. A score for a candidate transcription for the utterance is generated using the selected domain-specific model component and a baseline model component of the language model that is domain-independent. A transcription for the utterance is determined using the score the transcription is provided as output of an automated speech recognition system.

公开(公告)号：US10832664B2

公开(公告)日：2020-11-10

申请号：US15682133

申请日：2017-08-21

Applicant: Google LLC

Inventor： Fadi Biadsy ,  Diamantino Antionio Caseiro

IPC: G06F17/27 ,  G10L15/197 ,  G10L15/02 ,  G10L15/32 ,  G10L15/18 ,  G10L15/183 ,  G10L15/19

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for language models using domain-specific model components. In some implementations, context data for an utterance is obtained. A domain-specific model component is selected from among multiple domain-specific model components of a language model based on the non-linguistic context of the utterance. A score for a candidate transcription for the utterance is generated using the selected domain-specific model component and a baseline model component of the language model that is domain-independent. A transcription for the utterance is determined using the score the transcription is provided as output of an automated speech recognition system.