Invention Grant
- Patent Title: Neural network based acoustic models for speech recognition by grouping context-dependent targets
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Application No.: US15226478Application Date: 2016-08-02
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Publication No.: US11263516B2Publication Date: 2022-03-01
- Inventor: Gakuto Kurata
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Current Assignee Address: US NY Armonk
- Agency: Tutunjian & Bitetto, P.C.
- Agent Randall Bluestone
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N7/00 ; G06N3/08 ; G10L15/16 ; G10L15/06

Abstract:
Methods and systems for training a neural network include identifying weights in a neural network between a final hidden neuron layer and an output neuron layer that correspond to state matches between a neuron of the final hidden neuron layer and a respective neuron of the output neuron layer. The identified weights are initialized to a predetermined non-zero value and initializing other weights between the final hidden neuron layer and the output neuron layer to zero. The neural network is trained based on a training corpus after initialization.
Public/Granted literature
- US20180039883A1 NEURAL NETWORK BASED ACOUSTIC MODELS FOR SPEECH RECOGNITION BY GROUPING CONTEXT-DEPENDENT TARGETS Public/Granted day:2018-02-08
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