Modeling Dyadic and Group Impressions with Intermodal and Interperson Features
| Autor: | Laurent Son Nguyen, Daniel Gatica-Perez, Oya Aran, Shogo Okada |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Communicative competence
Computer Networks and Communications business.industry Computer science Inference 020207 software engineering 02 engineering and technology computer.software_genre Multimodal interaction Nonverbal communication Binary classification Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Interactor Big Five personality traits business Cluster analysis computer Natural language processing |
| Zdroj: | ACM Transactions on Multimedia Computing, Communications, and Applications. 15:1-30 |
| ISSN: | 1551-6865 1551-6857 |
| DOI: | 10.1145/3265754 |
| Popis: | This article proposes a novel feature-extraction framework for inferring impression personality traits, emergent leadership skills, communicative competence, and hiring decisions. The proposed framework extracts multimodal features, describing each participant’s nonverbal activities. It captures intermodal and interperson relationships in interactions and captures how the target interactor generates nonverbal behavior when other interactors also generate nonverbal behavior. The intermodal and interperson patterns are identified as frequent co-occurring events based on clustering from multimodal sequences. The proposed framework is applied to the SONVB corpus, which is an audiovisual dataset collected from dyadic job interviews, and the ELEA audiovisual data corpus, which is a dataset collected from group meetings. We evaluate the framework on a binary classification task involving 15 impression variables from the two data corpora. The experimental results show that the model trained with co-occurrence features is more accurate than previous models for 14 out of 15 traits. |
| Databáze: | OpenAIRE |
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