SYSTEMS AND METHODS FOR COMBINING STOCHASTIC AVERAGE GRADIENT AND HESSIAN-FREE OPTIMIZATION FOR SEQUENCE TRAINING OF DEEP NEURAL NETWORKS

A method for training a deep neural network (DNN), comprises receiving and formatting speech data for the training, performing Hessian-free sequence training (HFST) on a first subset of a plurality of subsets of the speech data, and iteratively performing the HFST on successive subsets of the plural...

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Bibliographic Details
Main Authors DOGNIN PIERRE, GOEL VAIBHAVA
Format Patent
LanguageEnglish
Published 29.10.2015
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A method for training a deep neural network (DNN), comprises receiving and formatting speech data for the training, performing Hessian-free sequence training (HFST) on a first subset of a plurality of subsets of the speech data, and iteratively performing the HFST on successive subsets of the plurality of subsets of the speech data, wherein iteratively performing the HFST comprises reusing information from at least one previous iteration.
Bibliography:Application Number: US201514793095