Principes et outils pour l’annotation des corpus

Amoyal, Mary; Bertrand, Roxane; Bigi, Brigitte; Boudin, Auriane; Meunier, Christine; Pallaud, Berthille; Priego-Valverde, Béatrice; Rauzy, Stéphane

doi:10.4000/tipa.5424

Cited by 1 publication

(2 citation statements)

References 35 publications

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“…As a result, phonetic, syntactic, semantic and pragmatic information progressively becomes aligned. The hypothesis is that listeners actively process simultaneously these sources of information in order to understand the main speaker's production and be prepared to produce a feedback 1 (or take the turn) appropriately. However, it should be noted that this hypothesis has yet to be definitively proven through empirical research during spontaneous conversations.…”

Section: A Rich Multimodal Synchronized Dataset For Studying Conversa...mentioning

confidence: 99%

“…The morphosyntactic information is obtained by running the MarsaTag POS-tagger [35] on the manually corrected transcription. For spoken French, the MarsaTag system has been trained on the corpus CID [7] (8-hours of French dyadic conversations) and allows to manage phenomena proper to spontaneous speech such as filled pause, disfluency and truncation [1].…”

Section: Automatic Annotationmentioning

confidence: 99%

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SMYLE: A new multimodal resource of talk-in-interaction including neuro-physiological signal

Boudin,

Bertrand,

Rauzy

et al. 2023

International Cconference on Multimodal Interaction

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This article presents the SMYLE corpus, the first multimodal corpus in French (16h) including neuro-physiological data from 60 participants engaged in face-to-face storytelling (8.2h) and free conversation tasks (7.8h). The originality of this corpus lies first in the fact that it bears all modalities, precisely synchronized and second in the addition for the first time at this scale of neuro-physiological modalities. It constitutes the first corpus of this size offering the opportunity to investigate cognitive characteristics of spontaneous conversation including at the brain level. The storytelling task comprises two conditions: a storyteller talking with a "normal" or a "distracted" listener. Contrasting normal and disrupted conversations allows to study at a behavioral, linguistic and cognitive levels the complex characteristics and organization of conversations.In this article, we present first the methodology developed to acquire and synchronize the different sources and types of signal. In a second part, we detail the large set of automatic, semi-automatic and manual annotations of the complete dataset. In a last section,

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Section: A Rich Multimodal Synchronized Dataset For Studying Conversa...mentioning

confidence: 99%

Section: Automatic Annotationmentioning

confidence: 99%