Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes
International audience 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 pres...
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ftccsdartic:oai:HAL:hal-01074879v1 2023-11-12T04:19:16+01:00 Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes Chollet, Mathieu Ochs, Magalie Pelachaud, Catherine Laboratoire Traitement et Communication de l'Information (LTCI) Télécom ParisTech-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS) Reykjavik, Iceland 2014-05 https://hal.science/hal-01074879 https://hal.science/hal-01074879/document https://hal.science/hal-01074879/file/lreccamerareadyfinal.pdf en eng HAL CCSD hal-01074879 https://hal.science/hal-01074879 https://hal.science/hal-01074879/document https://hal.science/hal-01074879/file/lreccamerareadyfinal.pdf info:eu-repo/semantics/OpenAccess International Conference on Language Resources and Evaluation https://hal.science/hal-01074879 International Conference on Language Resources and Evaluation, May 2014, Reykjavik, Iceland Non-verbal behavior Interpersonal attitudes Sequence mining [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] info:eu-repo/semantics/conferenceObject Conference papers 2014 ftccsdartic 2023-10-14T23:41:03Z International audience 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. Conference Object Iceland Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
Non-verbal behavior Interpersonal attitudes Sequence mining [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] |
spellingShingle |
Non-verbal behavior Interpersonal attitudes Sequence mining [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] Chollet, Mathieu Ochs, Magalie Pelachaud, Catherine Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
topic_facet |
Non-verbal behavior Interpersonal attitudes Sequence mining [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] |
description |
International audience 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. |
author2 |
Laboratoire Traitement et Communication de l'Information (LTCI) Télécom ParisTech-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS) |
format |
Conference Object |
author |
Chollet, Mathieu Ochs, Magalie Pelachaud, Catherine |
author_facet |
Chollet, Mathieu Ochs, Magalie Pelachaud, Catherine |
author_sort |
Chollet, Mathieu |
title |
Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
title_short |
Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
title_full |
Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
title_fullStr |
Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
title_full_unstemmed |
Mining a Multimodal Corpus for Non-Verbal Signals Sequences Conveying Attitudes |
title_sort |
mining a multimodal corpus for non-verbal signals sequences conveying attitudes |
publisher |
HAL CCSD |
publishDate |
2014 |
url |
https://hal.science/hal-01074879 https://hal.science/hal-01074879/document https://hal.science/hal-01074879/file/lreccamerareadyfinal.pdf |
op_coverage |
Reykjavik, Iceland |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
International Conference on Language Resources and Evaluation https://hal.science/hal-01074879 International Conference on Language Resources and Evaluation, May 2014, Reykjavik, Iceland |
op_relation |
hal-01074879 https://hal.science/hal-01074879 https://hal.science/hal-01074879/document https://hal.science/hal-01074879/file/lreccamerareadyfinal.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1782335756067405824 |