Social skills represent a fundamental resource in any professional and personal situation for conducting smooth interactions. Methods in Artificial Intelligence have become increasingly popular in order to automatically assess social skills -- by analyzing multi-modal behavior in several contexts such as public speaking, job interviews, group interactions, in populations with dysfunctions and involving both human-human and human-machine interactions. These methods could facilitate planning interventions targeted at improving these competencies, for example by giving appropriate feedback and personalized training.
The first step to investigate multi-modal cues of social skills, no matter the context, often consists in the analysis of corpora, by automatically extracting behavioral features and manually annotating more subjective and psychological constructs. Finding an existing corpus that could be exploited for one's research interest could be a difficult step, either because of difficulties in obtaining access, or because they are not adapted for different research goals. Often, researchers prefer to create their own corpus. This results in a large amount of existing corpora that are often not accessible to other researchers and are not fully exploited. The main challenges related to corpus creation include the choice of the best setup and sensors, finding a trade-off between eliciting natural interactions, limiting invasiveness and collecting precise information.
The second issue in this context regards the process of annotation. The choice of the type of annotators (experts vs. non-experts), the type of annotations (automatic vs. manual, continue vs. discrete), the temporal segmentation (windowed vs. holistic) is crucial for a correct measure of the phenomenon of interest and getting significant results.
The goal of this Research Topic is to stimulate a multi-disciplinary discussion about these challenges and sharing the best practices for analyzing social skills behavior. Contributions from computer science, psychological and psychometrics perspectives, as well as applications including platforms to share corpora and annotations, are welcomed.
Topics centered around the challenges related to social skills annotation of multi-modal behavior analysis, in the context of human-human, human-agent or human-robot interaction, including but not limited to:
-New Multi-modal corpora for studying social skills
-Review of existing work about social skills analysis
-Novel techniques to extract and annotate verbal and non-verbal behavior
-Annotation of subjective constructs related to social skills
-Annotation Tools
-Integration of existing corpora with annotations for social skills analysis
-Annotation schemes
-Data transformation and manipulation
Social skills represent a fundamental resource in any professional and personal situation for conducting smooth interactions. Methods in Artificial Intelligence have become increasingly popular in order to automatically assess social skills -- by analyzing multi-modal behavior in several contexts such as public speaking, job interviews, group interactions, in populations with dysfunctions and involving both human-human and human-machine interactions. These methods could facilitate planning interventions targeted at improving these competencies, for example by giving appropriate feedback and personalized training.
The first step to investigate multi-modal cues of social skills, no matter the context, often consists in the analysis of corpora, by automatically extracting behavioral features and manually annotating more subjective and psychological constructs. Finding an existing corpus that could be exploited for one's research interest could be a difficult step, either because of difficulties in obtaining access, or because they are not adapted for different research goals. Often, researchers prefer to create their own corpus. This results in a large amount of existing corpora that are often not accessible to other researchers and are not fully exploited. The main challenges related to corpus creation include the choice of the best setup and sensors, finding a trade-off between eliciting natural interactions, limiting invasiveness and collecting precise information.
The second issue in this context regards the process of annotation. The choice of the type of annotators (experts vs. non-experts), the type of annotations (automatic vs. manual, continue vs. discrete), the temporal segmentation (windowed vs. holistic) is crucial for a correct measure of the phenomenon of interest and getting significant results.
The goal of this Research Topic is to stimulate a multi-disciplinary discussion about these challenges and sharing the best practices for analyzing social skills behavior. Contributions from computer science, psychological and psychometrics perspectives, as well as applications including platforms to share corpora and annotations, are welcomed.
Topics centered around the challenges related to social skills annotation of multi-modal behavior analysis, in the context of human-human, human-agent or human-robot interaction, including but not limited to:
-New Multi-modal corpora for studying social skills
-Review of existing work about social skills analysis
-Novel techniques to extract and annotate verbal and non-verbal behavior
-Annotation of subjective constructs related to social skills
-Annotation Tools
-Integration of existing corpora with annotations for social skills analysis
-Annotation schemes
-Data transformation and manipulation
