公开(公告)号：US20230317082A1

公开(公告)日：2023-10-05

申请号：US17710137

申请日：2022-03-31

Applicant: GOOGLE LLC

Inventor： Om Dipakbhai Thakkar ,  Hakim Sidahmed ,  W. Ronny Huang ,  Rajiv Mathews ,  Françoise Beaufays ,  Florian Tramèr

IPC: G10L15/26 ,  G10L15/06 ,  G10L13/02

CPC classification number: G10L15/26 ,  G10L15/063 ,  G10L13/02

Abstract: An unintentional memorization measure can be used to determine whether an automatic speech recognition (ASR) model has unintentionally memorized one or more phrases during training of the ASR model. Various implementations include generating one or more candidate transcripts based on the vocabulary of the ASR model. For example, the system can generate a candidate transcript by appending a token of the vocabulary to a previous candidate transcript. Various implementations include processing the candidate transcript using a speech synthesis model to generate synthesized speech audio data that includes synthesized speech of the candidate transcript. Additionally or alternatively, the synthesized speech audio data can be processed using the ASR model to generate ASR output. Various implementations can include generating a loss based on comparing the ASR output and the candidate transcript.

公开(公告)号：US20230214642A1

公开(公告)日：2023-07-06

申请号：US17568933

申请日：2022-01-05

Applicant: Google LLC

Inventor： Hakim Sidahmed ,  Zheng Xu ,  Mingqing Chen ,  Yuan Cao ,  Ankush Garg

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Example aspects of the present disclosure provide a novel, resource-efficient approach for federated machine learning techniques with PTNs. The system can determine a first set of training parameters from a plurality of parameters of the global model. Additionally, the system can generate a random seed, using a random number generator, based on a set of frozen parameters. Moreover, the system can transmit, respectively to a plurality of client computing devices, a first set of training parameters and the random seed. Furthermore, the system can receive, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters. Subsequently, the system can aggregate the updates to one or more parameters that are respectively received from the plurality of client computing devices. The system can modify one or more global parameters of the global model based on the aggregation.