Summary of the paper
| Title | Building a System for Emotions Detection from Speech to Control an Affective Avatar |
|---|---|
| Authors | Mátyás Brendel, Riccardo Zaccarelli and Laurence Devillers |
| Abstract | In this paper we describe a corpus set together from two sub-corpora. TheCINEMO corpus contains acted emotional expression obtained by playing dubbingexercises. This new protocol is a way to collect mood-induced data in large amount which show severalcomplex and shaded emotions. JEMO is a corpus collected with anemotion-detection game and contains more prototypical emotions than CINEMO. We show how the two sub-corpora balance and enrich eachother and result in a better performance. We built male and female emotionmodels and use Sequential Fast Forward Feature Selection to improve detection performances. After feature-selection weobtain good results even with our strict speaker independent testing method.The global corpus contains 88 speakers(38 females, 50 males). This study has been done within the scope of the ANR(National Research Agency) Affective Avatar project which deals with building asystem of emotions detection for monitoring an Artificial Agent by voice. |
| Language | Evaluation methodologies |
| Topics | Emotion Recognition/Generation, Corpus (creation, annotation, etc.), Evaluation methodologies |
Full paper  |
Building a System for Emotions Detection from Speech to Control an Affective Avatar |
| Bibtex | @InProceedings{BRENDEL10.403, author = {Mátyás Brendel, Riccardo Zaccarelli and Laurence Devillers}, title = {Building a System for Emotions Detection from Speech to Control an Affective Avatar}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |