Invention Grant
- Patent Title: Statistical-analysis-based reset of recurrent neural networks for automatic speech recognition
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Application No.: US15637559Application Date: 2017-06-29
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Publication No.: US10255909B2Publication Date: 2019-04-09
- Inventor: Joachim Hofer , Josef G. Bauer , Piotr Rozen , Georg Stemmer
- Applicant: INTEL IP CORPORATION
- Applicant Address: US CA Santa Clara
- Assignee: INTEL IP CORPORATION
- Current Assignee: INTEL IP CORPORATION
- Current Assignee Address: US CA Santa Clara
- Agency: Finch & Maloney PLLC
- Main IPC: G10L15/16
- IPC: G10L15/16 ; G10L15/14 ; G10L25/30 ; G10L15/26

Abstract:
Techniques are provided for calculating reset parameters for recurrent neural networks (RNN). A methodology implementing the techniques according to an embodiment includes generating a sequence of statistics. The calculation of each statistic is based on outputs of an RNN that is periodically re-initialized at a selected RNN reset time such that each of the calculated statistics is associated with a unique RNN reset time selected from a pre-determined range of reset times. The method further includes analyzing the sequence to identify a maximum interval during which the sequence remains relatively constant. The method further includes selecting a reset time parameter and reset context duration parameter, for re-initialization of the RNN during operation. The reset time parameter is based on the duration of the identified maximum interval and the reset context duration parameter is based on a time associated with the starting point of the identified maximum interval.
Public/Granted literature
- US20190005945A1 STATISTICAL-ANALYSIS-BASED RESET OF RECURRENT NEURAL NETWORKS FOR AUTOMATIC SPEECH RECOGNITION Public/Granted day:2019-01-03
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