Sourire pour négocier des transitions thématiques de conversations en Français

International audienceThis study focuses on participants’ smiling behavior as a resource for negotiating topic transitions in French conversations. The smile will be analyzed as a resource during topic transitions: through its intensities and its development. This study will show that the speaker’s smiling dynamic contributes to initiating a transition and that the hearer tends to synchronize his/her smile with the speaker to ratify it

Quelle méthodologie pour analyser le sourire lors d'interactions conversationnelles ?

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Analyse du sourire lors des transitions thématiques dans la conversation

Dans cette étude, nous explorons les mécanismes de la conversation à travers les transitions thématiques. Ces transitions constituent des moments particulièrement riches en négociation. L’objectif de ce travail est de comprendre quelles stratégies sont déployées par les interactants pour réussir a se mettre d’accord sur un sujet de discussion. La question est de savoir quel rôle a le sourire dans la construction de ces transitions thématiques. Nous interrogeons par une analyse multimodale, le rôle et l’impact du sourire sur l’ajustement des interlocuteurs. Cette mimique faciale est analysée à travers son intensité et son développement graduel. Nous avons ainsi pu explorer la simultanéité des sourires des interactants, dans le but de rendre compte de la collaboration dont les interlocuteurs font preuve. Cette étude permet de montrer que le sourire contribue au phénomène de convergence interactionnelle. Plus généralement, ce travail participe à une meilleure compréhension de l’organisation de la conversation a travers sa construction thématique

Quelle méthodologie pour analyser le sourire lors d'interactions conversationnelles ?

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Smiling in thematic transitions : multimodal analysis of conversational interactions depending on the relationship of the interactants

Cette thèse porte sur les sourires dans les transitions thématiques de conversations. Ces moments charnières impliquent la collaboration des participants : nous avons étudié les sourires comme ressources de cette collaboration. Nous avons également étudié l'effet de la relation des interactants sur cette collaboration. 40 interactions issues de deux corpus CHEESE ! et PACO ont été étudiées. Dans le premier corpus, les participants se connaissent, tandis que dans le second, ils se rencontrent pour la première fois le jour de l'enregistrement. Les sourires des interactants ont été annotés selon le SIS. Grâce à l'outil de détection automatique de sourire SMAD, développé pour les besoins de cette thèse, nous avons effectué une analyse fine du déploiement des trois intensités de sourire. Nos analyses ont consisté à articuler deux approches complémentaires : celles de l'Analyse Conversationnelle et de la Linguistique Interactionnelle via des analyses séquentielles ainsi que l'approche de la Linguistique de Corpus qui a consisté à interroger quantitativement nos données. Cette méthodologie nous a permis d'identifier trois résultats principaux. (1) Les phases de transitions sont caractérisées par des processus spécifiques, dont certains sont invariants quelle que soit la relation entre les participants. (2) Lorsque le locuteur initie un nouveau thème, une suppression du sourire est plus fréquemment observée. (3) Lorsque l'interlocuteur accepte la proposition thématique, il est plus enclin à augmenter l'intensité de son sourire. Cette thèse montre que le sourire est une ressource mobilisée par les interactants lorsqu'ils effectuent des transitions thématiques.This thesis focuses on smiles in thematic transitions of conversations. These moments of negotiation between two themes involve the the participants' collaboration. We have studied the role of smiles as ressources of this collaboration. Smiles are very frequent facial expressions but have not been studied in their relation to the organization of the interaction. In parallel, we studied the effect of the interactants' relationship on this collaboration. To explore these issues, 40 interactions from two corpora CHEESE! and PACO were studied. In the first corpus, the participants know each other, while in the second they met for the first time on the recording day. Smiles of the interactants were annotated according to the SIS. Thanks to the automatic smile detection tool SMAD, developed for the purpose of this thesis, we conducted a fine-grained analysis of the deployment of the three smile intensities. Our analyses consisted in articulating two complementary approaches: those of Conversational Analysis and Interactional Linguistics via sequential analyses as well as the approach of Corpus Linguistics which consisted in quantitatively interrogating our data. This methodology allowed us to identify three main results. (1) The phases of transitions are characterized by specific processes, some of which are invariant regardless of the participants' relationship. (2) When the speaker initiates a new topic, a suppression of the smile is more frequently observed. (3) When the addressee accepts the thematic proposal, he is more inclined to increase his smile intensity. This thesis shows that the smile is a resource mobilized by the interactants when they make thematic transitions

Automatic tool to annotate smile intensities in conversational face-to-face interactions

International audienceThis study presents an automatic tool that allows to trace smile intensities along a video record of conversational face-to-face interactions. The processed output proposes a sequence of adjusted time intervals labeled following the Smiling Intensity Scale ( Gironzetti, Attardo, and Pickering, 2016 ), a 5 levels scale varying from neutral facial expression to laughing smile. The underlying statistical model of this tool is trained on a manually annotated corpus of conversations featuring spontaneous facial expressions. This model will be detailed in this study. This tool can be used with benefits for annotating smile in interactions. The results are twofold. First, the evaluation reveals an observed agreement of 68% between manual and automatic annotations. Second, manually correcting the labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent for manually annotating smile intensities without pretreatment. Our annotation engine makes use of the state-of-the-art toolbox OpenFace for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. The documentation and the scripts of our tool, the SMAD software, are available to download at the HMAD open source project URL page https://github.com/srauzy/HMAD

SMAD: A tool for automatically annotating the smile intensity along a video record

International audienceWe present an automatic tool for tracing the dynamic of the smile intensity along a video record. The processed output consist in a sequence of adjusted time intervals labeled following the Smiling Intensity Scale of Gironzetti et al. (2016) [3], a 5 levels scale varying from neutral facial expression to laughing smile. The state-of-the-art toolbox OpenFace [2] is firstly used for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. In a second step the smile intensity automatic annotation is performed based on these OpenFace Action Units measurements. The statistical model underlying our SMAD tool is trained on a 1 hour manually annotated smiles of the CHEESE! corpus [4] (a full description of the model will be found in Rauzy & Amoyal, submitted to JMUI).The evaluation of the engine reveals an observed agreement of 68% between manual and automatic annotations. A more concrete experiment conducted on in-the-wild video records shows that manually correcting the labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent for manually annotating smile intensities without pretreatment. The smile annotation of PACO [1], a 5 hours corpus of conversational data built up for analyzing the impact of common ground in spontaneous face-to-face interaction, has already benefited from this gain in annotation time.The SMAD scripts and documentation are available to download at the HMAD open source project url page https : //github:com/srauzy/HMAD.References:[1] Amoyal M, Priego-Valverde B, Rauzy S (2020) PACO : A corpus to analyze the impact of common ground in spontaneous face-to-face interaction. In: Language Resources and Evaluation Conference, LREC 2020, Marseille, France[2] Baltruisaitis T, Zadeh A, Lim YC, Morency LP (2018) Openface 2.0: Facial behavior analysis toolkit. In: 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp 59-66[3] Gironzetti E, Attardo S, Pickering L (2016) Smiling, gaze, and humor in conversation: A pilot study. In: Ruiz-Gurillo L (ed) Metapragmatics of Humor: Current research trends, pp 235-254[4] Priego-Valverde B, Bigi B, Attardo S, Pickering L, Gironzetti E (2018) Is smiling during humor so obvious? A cross-cultural comparison of smiling behavior in humorous sequences in American English and French interactions. Intercultural Pragmatic

Le sourire nous parle

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De l’écologie à l’écologisme de Marx :

La récente publication par les Éditions Amsterdam d’une sélection d’articles de John Bellamy Foster sous le titre Marx écologiste (2011) nous offre l’occasion de nous pencher sur un courant de la pensée sociale qui reste trop souvent méconnu en France. Autour de l’appellation un peu vague, quoique stimulante, d’« écosocialisme », on a pris l’habitude de rassembler les contributions théoriques qui cherchent à construire l’enjeu écologique dans le prolo..

Sourire pour négocier des transitions thématiques de conversations en Français

International audienceThis study focuses on participants’ smiling behavior as a resource for negotiating topic transitions in French conversations. The smile will be analyzed as a resource during topic transitions: through its intensities and its development. This study will show that the speaker’s smiling dynamic contributes to initiating a transition and that the hearer tends to synchronize his/her smile with the speaker to ratify it