The Vocal Effort of Dominance in Scenario Meetings

In this paper we address two questions about dominance in the AMI-IDIAP scenario meetings: (i) do the annotated most and least dominant utterances correlate with different levels of vocal effort? and if so (ii) how quantitatively discriminative are the vocal effort effects for prosody, voice quality and low level acoustic features? For answering these questions we perform supervised learning with dominance annotations in AMI-IDIAP meetings and vocal effort annotations in controlled data. A linear discriminant analysis (LDA) classifier is used to optimise class separability. We have found that the most and least dominant utterances are acoustically correlated with loud and soft vocal effort. We were able to quantify around 55% discrimination of equal distributions of most dominant, neutral and least dominant utterances using low level acoustic measures.