Investigating the Prosody and Voice Quality of Social Signals in Scenario Meetings

Abstract. In this study we propose a methodology to investigate possible prosody and voice quality correlates of social signals, and test-run it on annotated naturalistic recordings of scenario meetings. The core method consists of computing a set of prosody and voice quality measures, followed by a Principal Components Analysis (PCA) and Support Vector Machine (SVM) classification to identify the core factors predicting the associated social signal or related annotation. We apply the methodology to controlled data and two types of annotations in the AMI meeting corpus that are relevant for social signalling: dialogue acts and speaker roles.

• Type: Journal
• Year: 2011
• Where: ACII
• Authors: Marcela Charfuelan, Marc Schröder
• Keywords: ACII 2011, Applied Computing, Dialogue Acts, Principal Components Analysis, Support Vector Machine