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NII Technical Report (NII-2017-003E)：A simple RNN-plus-highway network for statistical parametric speech synthesis Wang, Xin 高木, 信二 Takaki, Shinji 山岸, 順一 Yamagishi, Junichi テクニカルレポート Technical Report 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. 国立情報学研究所 2017-04-27 eng departmental bulletin paper https://doi.org/10.20736/0002000365 https://repository.nii.ac.jp/records/2000365 10.20736/0002000365 1346-5597 NIIテクニカル・レポート NII Technical Report 1 8 https://repository.nii.ac.jp/record/2000365/files/17-003E.pdf application/pdf 463 KB 2022-06-09