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. The CINEMO corpus contains acted emotional expression obtained by playing dubbing exercises. This new protocol is a way to collect mood-induced data in large amount which show several complex and shaded emotions. JEMO is a corpus collected with an emotion-detection game and contains more prototypical emotions than CINEMO. We show how the two sub-corpora balance and enrich each other and result in a better performance. We built male and female emotion models and use Sequential Fast Forward Feature Selection to improve detection performances. After feature-selection we obtain 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 a system of emotions detection for monitoring an Artificial Agent by voice. |
| 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 |
Slides |
Building a System for Emotions Detection from Speech to Control an Affective Avatar |
| Bibtex | @InProceedings{BRENDEL10.403, author = {Mátyás Brendel and Riccardo Zaccarelli and Laurence Devillers}, title = {Building a System for Emotions Detection from Speech to Control an Affective Avatar}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |