- Patent Title: Asynchronous optimization for sequence training of neural networks
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Application No.: US17143140Application Date: 2021-01-06
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Publication No.: US11227582B2Publication Date: 2022-01-18
- Inventor: Georg Heigold , Erik Mcdermott , Vincent O. Vanhoucke , Andrew W. Senior , Michiel A. U. Bacchiani
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Honigman LLP
- Agent Brett A. Krueger; Grant Griffith
- Main IPC: G10L15/06
- IPC: G10L15/06 ; G10L15/16 ; G10L15/183 ; G06N3/04

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.
Public/Granted literature
- US20210125601A1 ASYNCHRONOUS OPTIMIZATION FOR SEQUENCE TRAINING OF NEURAL NETWORKS Public/Granted day:2021-04-29
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