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    <abstract>Dialogue systems need to accurately understand the user‘s mental state to generate appropriate responses, but accurately discerning such states solely from text or speech can be challenging. To determine which information is necessary, we first collected human-human multimodal dialogues using heterogeneous sensors, resulting in a dataset containing various types of information including speech, video, physiological signals, gaze, and body movement. Additionally, for each time step of the data, users provided subjective evaluations of their emotional valence while reviewing the dialogue videos. Using this dataset and focusing on physiological signals, we analyzed the relationship between the signals and the subjective evaluations through Granger causality analysis. We also investigated how sensor signals differ depending on the polarity of the valence. Our findings revealed several physiological signals related to the user‘s emotional valence.</abstract>
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%0 Conference Proceedings
%T Estimating the Emotional Valence of Interlocutors Using Heterogeneous Sensors in Human-Human Dialogue
%A Jiang, Jingjing
%A Guo, Ao
%A Higashinaka, Ryuichiro
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F jiang-etal-2024-estimating
%X Dialogue systems need to accurately understand the user‘s mental state to generate appropriate responses, but accurately discerning such states solely from text or speech can be challenging. To determine which information is necessary, we first collected human-human multimodal dialogues using heterogeneous sensors, resulting in a dataset containing various types of information including speech, video, physiological signals, gaze, and body movement. Additionally, for each time step of the data, users provided subjective evaluations of their emotional valence while reviewing the dialogue videos. Using this dataset and focusing on physiological signals, we analyzed the relationship between the signals and the subjective evaluations through Granger causality analysis. We also investigated how sensor signals differ depending on the polarity of the valence. Our findings revealed several physiological signals related to the user‘s emotional valence.
%R 10.18653/v1/2024.sigdial-1.61
%U https://aclanthology.org/2024.sigdial-1.61/
%U https://doi.org/10.18653/v1/2024.sigdial-1.61
%P 718-727

Markdown (Informal)

[Estimating the Emotional Valence of Interlocutors Using Heterogeneous Sensors in Human-Human Dialogue](https://aclanthology.org/2024.sigdial-1.61/) (Jiang et al., SIGDIAL 2024)

• Estimating the Emotional Valence of Interlocutors Using Heterogeneous Sensors in Human-Human Dialogue (Jiang et al., SIGDIAL 2024)

ACL

• Jingjing Jiang, Ao Guo, and Ryuichiro Higashinaka. 2024. Estimating the Emotional Valence of Interlocutors Using Heterogeneous Sensors in Human-Human Dialogue. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 718–727, Kyoto, Japan. Association for Computational Linguistics.