Annotation of expressive dimensions on a multimodal French corpus of political interviews

Jules Cauzinille, Marc Evrard, Nikita Kiselov, Albert Rilliard


Abstract
We present a French corpus of political interviews labeled at the utterance level according to expressive dimensions such as Arousal. This corpus consists of 7.5 hours of high-quality audio-visual recordings with transcription. At the time of this publication, 1 hour of speech was segmented into short utterances, each manually annotated in Arousal. Our segmentation approach differs from similar corpora and allows us to perform an automatic Arousal prediction baseline by building a speech-based classification model. Although this paper focuses on the acoustic expression of Arousal, it paves the way for future work on conflictual and hostile expression recognition as well as multimodal architectures.
Anthology ID:
2022.politicalnlp-1.13
Volume:
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Haithem Afli, Mehwish Alam, Houda Bouamor, Cristina Blasi Casagran, Colleen Boland, Sahar Ghannay
Venue:
PoliticalNLP
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
91–97
Language:
URL:
https://aclanthology.org/2022.politicalnlp-1.13
DOI:
Bibkey:
Cite (ACL):
Jules Cauzinille, Marc Evrard, Nikita Kiselov, and Albert Rilliard. 2022. Annotation of expressive dimensions on a multimodal French corpus of political interviews. In Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences, pages 91–97, Marseille, France. European Language Resources Association.
Cite (Informal):
Annotation of expressive dimensions on a multimodal French corpus of political interviews (Cauzinille et al., PoliticalNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.politicalnlp-1.13.pdf