Invention Grant
- Patent Title: Connectionist temporal classification using segmented labeled sequence data
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Application No.: US15909930Application Date: 2018-03-01
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Publication No.: US10762427B2Publication Date: 2020-09-01
- Inventor: Saeed Mosayyebpour Kaskari , Trausti Thormundsson , Francesco Nesta
- Applicant: SYNAPTICS INCORPORATED
- Applicant Address: US CA San Jose
- Assignee: SYNAPTICS INCORPORATED
- Current Assignee: SYNAPTICS INCORPORATED
- Current Assignee Address: US CA San Jose
- Agency: Haynes and Boone, LLP
- Main IPC: G10L15/00
- IPC: G10L15/00 ; G06N3/08 ; G06K9/62 ; G06N3/04 ; G10L15/06 ; G10L15/16 ; G10L15/02 ; G06N7/00

Abstract:
Classification training systems and methods include a neural network for classification of input data, a training dataset providing segmented labeled training data, and a classification training module operable to train the neural network using the training data. A forward pass processing module is operable to generate neural network outputs for the training data using weights and bias for the neural network, and a backward pass processing module is operable to update the weights and biases in a backward pass, including obtaining Region of Target (ROT) information from the training data, generate a forward-backward masking based on the ROT information, the forward-backward masking placing at least one restriction on a neural network output path, compute modified forward and backward variables based on the neural network outputs and the forward-backward masking, and update the weights and biases.
Public/Granted literature
- US20180253648A1 CONNECTIONIST TEMPORAL CLASSIFICATION USING SEGMENTED LABELED SEQUENCE DATA Public/Granted day:2018-09-06
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