Patent ID: 9626621
Date: 2017-04-18
CPC Classifications: G06N,G10L

Claim:
1. A system for training a deep neural network, comprising: a memory and at least one processor coupled to the memory; an input component, executed via the at least one processor, which receives and formats speech data for the training, and divides the speech data into a plurality of subsets; a training component, executed via the at least one processor, which performs Hessian-free sequence training on a first subset of the plurality of subsets of the speech data received from the input component, and iteratively performs the Hessian-free sequence training on successive subsets of the plurality of subsets of the speech data; wherein, when iteratively performing the Hessian-free sequence training, the training component: a weighting component operatively coupled to the training component and executed via the at least one processor, which dynamically computes weights for the first gradient of loss and for the second gradient of loss; wherein the training component reuses gradient information from at least one previous iteration, and wherein reusing the gradient information from the at least one previous iteration comprises integrating a weighted first gradient of loss and a weighted second gradient of loss to generate a solution to the second iteration; and an output component, executed via the at least one processor, which transmits a result of the iterative performance of the Hessian-free sequence training to the deep neural network.