1. A training method for speech recognition using a deep neural network configured to identify speech objects classified to a plurality of categories utilizing, for each of said plurality of categories, a category-independent sub-network, the method comprising: storing on a non-transitory computer readable medium a first sub-network, a second sub-network, and a third sub-network; and training with a processor said first sub-network, said second sub-network, and said third sub-network with training data belonging to a first category and a second category of the plurality of categories; wherein training said first sub-network and said second sub-network includes: training a first deep neural network formed by connecting said second sub-network to an output side of said first sub-network with training data belonging to said first category, training a second deep neural network formed by connecting said third sub-network to an output side of said first sub-network with training data belonging to said second category, and separating, after completion of training of the first deep neural network and training of the second deep neural network, said first sub-network from other sub-networks and storing in the non-transitory computer readable medium said first sub-network as said category-independent sub-network.