Nous présentons un nouveau corpus, nommé AlloSat, composé de conversations en français extraites de centre d’appels, annotées de façon continue en frustration et satisfaction. Dans le contexte des centres d’appels, une conversation vise généralement à résoudre la demande de l’appelant. Ce corpus a été mis en place afin de développer de nouveaux systèmes capables de modéliser l’aspect continu de l’information sémantique et para-linguistique au niveau conversationnel. Nous nous concentrons sur le niveau para-linguistique, plus précisément sur l’expression des émotions. À notre connaissance, la plupart des corpus émotionnels contiennent des annotations en catégories discrètes ou dans des dimensions continues telles que l’activation ou la valence. Nous supposons que ces dimensions ne sont pas suffisamment liées à notre contexte. Pour résoudre ce problème, nous proposons un corpus permettant une connaissance en temps réel de l’axe frustration/satisfaction. AlloSat regroupe 303 conversations pour un total d’environ 37 heures d’audio, toutes enregistrées dans des environnements réels, collectées par Allo-Media (une société spécialisée dans l’analyse automatique d’appels). Les premières expériences de classification montrent que l’évolution de l’axe frustration/satisfaction peut être prédite automatiquement par conversation.

Manual annotation of speech corpora is expensive in both human resources and time. Furthermore, recognizing affects in spontaneous, non acted speech presents a challenge for humans and machines. The aim of the present study is to automatize the labeling of hesitant speech as a marker of expressed uncertainty. That is why, the NCCFr-corpus was manually annotated for ‘degree of hesitation’ on a continuous scale between -3 and 3 and the affective dimensions ‘activation, valence and control’. In total, 5834 chunks of the NCCFr-corpus were manually annotated. Acoustic analyses were carried out based on these annotations. Furthermore, regression models were trained in order to allow automatic prediction of hesitation for speech chunks that do not have a manual annotation. Preliminary results show that the number of filled pauses as well as vowel duration increase with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6.

We present a new corpus, named AlloSat, composed of real-life call center conversations in French that is continuously annotated in frustration and satisfaction. This corpus has been set up to develop new systems able to model the continuous aspect of semantic and paralinguistic information at the conversation level. The present work focuses on the paralinguistic level, more precisely on the expression of emotions. In the call center industry, the conversation usually aims at solving the caller’s request. As far as we know, most emotional databases contain static annotations in discrete categories or in dimensions such as activation or valence. We hypothesize that these dimensions are not task-related enough. Moreover, static annotations do not enable to explore the temporal evolution of emotional states. To solve this issue, we propose a corpus with a rich annotation scheme enabling a real-time investigation of the axis frustration / satisfaction. AlloSat regroups 303 conversations with a total of approximately 37 hours of audio, all recorded in real-life environments collected by Allo-Media (an intelligent call tracking company). First regression experiments, with audio features, show that the evolution of frustration / satisfaction axis can be retrieved automatically at the conversation level.

This article presents a corpus featuring children playing games in interaction with the humanoid robot Nao: children have to express emotions in the course of a storytelling by the robot. This corpus was collected to design an affective interactive system driven by an interactional and emotional representation of the user. We evaluate here some mid-level markers used in our system: reaction time, speech duration and intensity level. We also question the presence of affect bursts, which are quite numerous in our corpus, probably because of the young age of the children and the absence of predefined lexical content.