Summary of the paper

Title Building a Multimodal Laughter Database for Emotion Recognition
Authors Merlin Teodosia Suarez, Jocelynn Cu and Madelene Sta. Maria
Abstract Laughter is a significant paralinguistic cue that is largely ignored in multimodal affect analysis. In this work, we investigate how a multimodal laughter corpus can be constructed and annotated both with discrete and dimensional labels of emotions for acted and spontaneous laughter. Professional actors enacted emotions to produce acted clips, while spontaneous laughter was collected from volunteers. Experts annotated acted laughter clips, while volunteers who possess an acceptable empathic quotient score annotated spontaneous laughter clips. The data was pre-processed to remove noise from the environment, and then manually segmented starting from the onset of the expression until its offset. Our findings indicate that laughter carries distinct emotions, and that emotion in laughter is best recognized using audio information rather than facial information. This may be explained by emotion regulation, i.e. laughter is used to suppress or regulate certain emotions. Furthermore, contextual information plays a crucial role in understanding the kind of laughter and emotion in the enactment.
Topics Corpus (creation, annotation, etc.), Emotion Recognition/Generation, Other
Full paper Building a Multimodal Laughter Database for Emotion Recognition
Bibtex @InProceedings{SUAREZ12.779,
  author = {Merlin Teodosia Suarez and Jocelynn Cu and Madelene Sta. Maria},
  title = {Building a Multimodal Laughter Database for Emotion Recognition},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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