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タイトル: Character expression for spoken dialogue systems with semi-supervised learning using Variational Auto-Encoder
著者: Yamamoto, Kenta
Inoue, Koji  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-2929-2559 (unconfirmed)
Kawahara, Tatsuya  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-2686-2296 (unconfirmed)
著者名の別形: 山本, 賢太
井上, 昂治
河原, 達也
キーワード: Spoken dialogue system
Character
Semi-supervised learning
Variational auto-encoder (VAE)
発行日: Apr-2023
出版者: Elsevier BV
誌名: Computer Speech & Language
巻: 79
論文番号: 101469
抄録: Character of spoken dialogue systems is important not only for giving a positive impression of the system but also for gaining rapport from users. We have proposed a character expression model for spoken dialogue systems. The model expresses three character traits (extroversion, emotional instability, and politeness) of spoken dialogue systems by controlling spoken dialogue behaviors: utterance amount, backchannel, filler, and switching pause length. One major problem in training this model is that it is costly and time-consuming to collect many pair data of character traits and behaviors. To address this problem, semi-supervised learning is proposed based on a variational auto-encoder that exploits both the limited amount of labeled pair data and unlabeled corpus data. It was confirmed that the proposed model can express given characters more accurately than a baseline model with only supervised learning. We also implemented the character expression model in a spoken dialogue system for an autonomous android robot, and then conducted a subjective experiment with 75 university students to confirm the effectiveness of the character expression for specific dialogue scenarios. The results showed that expressing a character in accordance with the dialogue task by the proposed model improves the user’s impression of the appropriateness in formal dialogue such as job interview.
著作権等: © 2022 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license.
URI: http://hdl.handle.net/2433/281689
DOI(出版社版): 10.1016/j.csl.2022.101469
出現コレクション:学術雑誌掲載論文等

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