Automatic Estimation of Perceived Sincerity from Spoken Language

Sincerity is important in everyday human communication and perception of genuineness can greatly affect emotions and outcomes in social interactions. In this paper, submitted for the INTERSPEECH 2016 Sincerity Challenge, we examine a corpus of six different types of apologetic utterances from a variety of English speakers articulated in different prosodic styles, and we rate the sincerity of each remark. Since the utterances and semantic meaning in the examined database are controlled, we focus on tone of voice by exploring a plethora of acoustic and paralinguistic features not present in the baseline model and how well they contribute to human assessment of sincerity. We show that these additional features improve the performance using the baseline model, and furthermore that conditioning learning models on the prosody of utterances boosts the prediction accuracy. Our best system outperforms the challenge baseline and in principle can generalize well to other corpora.