Summary of the paper
| Title | Mining a Multimodal Corpus for Non-Verbal Behavior Sequences Conveying Attitudes |
|---|---|
| Authors | Mathieu Chollet, Magalie Ochs and Catherine Pelachaud |
| Abstract | Interpersonal attitudes are expressed by non-verbal behaviors on a variety of different modalities. The perception of these behaviors is influenced by how they are sequenced with other behaviors from the same person and behaviors from other interactants. In this paper, we present a method for extracting and generating sequences of non-verbal signals expressing interpersonal attitudes. These sequences are used as part of a framework for non-verbal expression with Embodied Conversational Agents that considers different features of non-verbal behavior: global behavior tendencies, interpersonal reactions, sequencing of non-verbal signals, and communicative intentions. Our method uses a sequence mining technique on an annotated multimodal corpus to extract sequences characteristic of different attitudes. New sequences of non-verbal signals are generated using a probabilistic model, and evaluated using the previously mined sequences. |
| Topics | Information Extraction, Information Retrieval, Statistical and Machine Learning Methods |
Full paper  |
Mining a Multimodal Corpus for Non-Verbal Behavior Sequences Conveying Attitudes |
| Bibtex | @InProceedings{CHOLLET14.235, author = {Mathieu Chollet and Magalie Ochs and Catherine Pelachaud}, title = {Mining a Multimodal Corpus for Non-Verbal Behavior Sequences Conveying Attitudes}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |