Language-Independent Socio-Emotional Role Recognition in the AMI Meetings Corpus

摘要

Social roles are a coding scheme that characterizes the relationships between group members during a discussion and their roles "oriented toward the functioning of the group as a group". This work presents an investigation on language-independent automatic social role recognition in AMI meetings based on turns statistics and prosodic features. At first, turn-taking statistics and prosodic features are integrated into a single generative conversation model which achieves a role recognition accuracy of 59%. This model is then extended to explicitly account for dependencies (or influence) between speakers achieving an accuracy of 65%. The last contribution consists in investigating the statistical dependencies between the formal and the social role that participants have; integrating the information related to the formal role in the model, the recognition achieves an accuracy of 68%.