@article{oai:repository.nii.ac.jp:02000365,
 author = {Wang, Xin and 高木, 信二 and Takaki, Shinji and 山岸, 順一 and Yamagishi, Junichi},
 journal = {NIIテクニカル・レポート, NII Technical Report},
 month = {Apr},
 note = {In this report, we proposes a neural network structure that combines a recurrent neural network (RNN) and a deep highway network. Compared with the highway RNN structures proposed in other studies, the one proposed in this study is simpler since it only concatenates a highway network after a pre-trained RNN. The main idea is to use the ‘iterative unrolled estimation’ of a highway network to finely change the output from the RNN. The experiments on the proposed network structure with a baseline RNN and 7 highway blocks demonstrated that this network performed relatively better than a deep RNN network with a similar mode size. Furthermore, it took less than half the training time of the deep RNN.},
 pages = {1--8},
 title = {NII Technical Report (NII-2017-003E)：A simple RNN-plus-highway network for statistical parametric speech synthesis},
 year = {2017}
}