公开(公告)号：US20240112673A1

公开(公告)日：2024-04-04

申请号：US17958887

申请日：2022-10-03

Applicant: GOOGLE LLC

Inventor： Rajiv Mathews ,  Rohit Prabhavalkar ,  Giovanni Motta ,  Mingqing Chen ,  Lillian Zhou ,  Dhruv Guliani ,  Harry Zhang ,  Trevor Strohman ,  Françoise Beaufays

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22 ,  G10L15/30

CPC classification number: G10L15/197 ,  G10L15/063 ,  G10L15/22 ,  G10L15/30 ,  G10L2015/0635

Abstract: Implementations described herein identify and correct automatic speech recognition (ASR) misrecognitions. For example, on-device processor(s) of a client device may generate a predicted textual segment that is predicted to correspond to spoken utterance of a user of the client device, and may receive further input that modifies the predicted textual segment to an alternate textual segment. Further, the on-device processor(s) may store these textual segments in on-device storage as a candidate correction pair, and transmit the candidate correction pair to a remote system. Moreover, remote processor(s) of the remote system may determine that the candidate correction pair is an actual correction pair, and may cause client devices to generate updates for a global ASR model for the candidate correction pair. Additionally, the remote processor(s) may distribute the global ASR model to the client devices and/or additional client devices.

公开(公告)号：US20240386318A1

公开(公告)日：2024-11-21

申请号：US18386431

申请日：2023-11-02

Applicant: GOOGLE LLC

Inventor： Yuxin Ding ,  Lillian Zhou ,  Mingqing Chen ,  Rajiv Mathews ,  Andrew Hard ,  Sean Augenstein

IPC: G06N20/00

Abstract: Implementations described herein are directed to techniques for mitigating and/or eliminating catastrophic forgetting of a global machine learning (ML) model during decentralized learning thereof. Remote processor(s) of a remote system can initially train a global ML model based on server data that is accessible by the remote system. In subsequent decentralized learning of the global ML model, the remote processor(s) can utilize various checkpoint averaging techniques. As described herein, these various checkpoint averaging techniques can include, but are not limited to, a static checkpoint averaging technique, a dynamic checkpoint averaging techniques, and/or a mixed centralized and decentralized training technique.