@inproceedings{1f89f074cf1647f9bb1bf1ecf48af11a,
title = "Filler prediction based on bidirectional LSTM for generation of natural response of spoken dialog",
abstract = "Most of the conventional response generation models do not generate speech disfluencies including fillers, because they are trained from a written language corpus. It is necessary to insert fillers to written sentences for training a response generation model for the spoken language. In this paper, we proposed the filler prediction model based on bidirectional LSTM (BLSTM). This approach can consider a whole utterance and model both positions and kinds of fillers simultaneously. The experiments showed that the proposed method surpasses the conventional approach in terms of the prediction accuracy.",
keywords = "dialog system, filler prediction, response generation, speech disfluencies",
author = "Yoshihiro Yamazaki and Yuya Chiba and Takashi Nose and Akinori Ito",
note = "Funding Information: A part of this work was supported by the JST COI Grant Number JPMJCE1303 and the JSPS Grant-in-Aid for Scientific Research JP17H00823 and JP20K19903. Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Global Conference on Consumer Electronics, GCCE 2020 ; Conference date: 13-10-2020 Through 16-10-2020",
year = "2020",
month = oct,
day = "13",
doi = "10.1109/GCCE50665.2020.9291867",
language = "English",
series = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "360--361",
booktitle = "2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020",
address = "United States",
}