METHOD AND DEVICE FOR LEARNING DEEP NEURAL NETWORK, AND DEVICE FOR LEARNING CATEGORY-INDEPENDENT SUB-NETWORK

摘要

Provided is a DNN learning method that can reduce DNN learning time using data belonging to a plurality of categories. The method includes the steps of training a language-independent sub-network 120 and language-dependent sub-networks 122 and 124 with training data of Japanese and English. This step includes: a first step of training a DNN obtained by connecting neurons in an output layer of the sub-network 120 with neurons in an input layer of sub-network 122 with Japanese training data; a step of forming a DNN by connecting the sub-network 124 in place of the sub-network 122 to the sub-network 120 and training it with English data; repeating these steps alternately until all training data ends; and after completion, separating the first sub-network 120 from other sub-networks and storing it as a category-independent sub-network in a storage medium.