Context-Centric Approach in Paralinguistic Affect Recognition System

As the field of paralinguistic affect recognition has become more mature, many researchers have shifted their approach from a single channel of affect manifestation to a multi-modal one in developing their affect recognition systems. In the spirit continuing this trend in multi-modal work, our work utilizes paralinguistic features of speech and contextual knowledge. Through our human study, we found that contextual knowledge had positive impact on a human’s affect recognition ability when combined with paralinguistic features of speech. In this research, we propose a novel architecture called Context-Based Paralinguistic Affect Recognition System (CxBPARS) that combines the traditional paralinguistic affect recognition approach using classification algorithms and the contextual knowledge related to the emotion elicitors and their environment. By combining the results of an Ada-Boost classifier and contextual modeling, we achieved an improvement in affect recognition accuracy from 29.5% (context free) to 53.0% (context dependent).