@inproceedings{cauzinille-etal-2022-annotation,
title = "Annotation of expressive dimensions on a multimodal {F}rench corpus of political interviews",
author = "Cauzinille, Jules and
Evrard, Marc and
Kiselov, Nikita and
Rilliard, Albert",
editor = "Afli, Haithem and
Alam, Mehwish and
Bouamor, Houda and
Casagran, Cristina Blasi and
Boland, Colleen and
Ghannay, Sahar",
booktitle = "Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.politicalnlp-1.13/",
pages = "91--97",
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."
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%0 Conference Proceedings
%T Annotation of expressive dimensions on a multimodal French corpus of political interviews
%A Cauzinille, Jules
%A Evrard, Marc
%A Kiselov, Nikita
%A Rilliard, Albert
%Y Afli, Haithem
%Y Alam, Mehwish
%Y Bouamor, Houda
%Y Casagran, Cristina Blasi
%Y Boland, Colleen
%Y Ghannay, Sahar
%S Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F cauzinille-etal-2022-annotation
%X 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.
%U https://aclanthology.org/2022.politicalnlp-1.13/
%P 91-97
Markdown (Informal)
[Annotation of expressive dimensions on a multimodal French corpus of political interviews](https://aclanthology.org/2022.politicalnlp-1.13/) (Cauzinille et al., PoliticalNLP 2022)
ACL