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).

Research in the field of Affective Computing on affect recognition through speech has used a “fishing expedition” approach. Although some frameworks could achieve certain success rates, many of these approaches missed the theory behind the underlying voice and speech production mechanism. In this work, we found some correlation among the acoustic parameters (paralinguistic/non-verbal speech content) in the physiological mechanism of voice production. Furthermore, we also found some correlation when analyzing their relationships statistically. Aligned with this finding, we implemented our framework using the K-Nearest Neighbors (KNN) algorithm. Although our work is still in its infancy, we believe this context-free approach will bring us forward toward creating an intelligent agent with affect recognition ability. This paper describes the problem, our approach and our results.

The Thirteenth Annual International Conference of the Florida Artificial Intelligence Research Society was held in Orlando, Florida, on 22 to 24 May. The conference included sessions on 11 topics. The session on validation, verification, and system certification was the most extensive. The conference also included panel discussions and invited talks by Subrata Dasgupta, Jim Hendler, and Janet Kolodner.

LBD is a curriculum consisting of prescribed exercises that teach children real-world skills by ciently, and replan after device faults having them perform several activities Conference of the Florida caused the original plan to become that are familiar to them. The cochairs of about the computer's role in the current The conference also had two panel the conference were Avelino Gonzalez, revolution in cognitive science. The first focused on modern University of Central Florida, and His talk came from a historical perspective--how trends in funding opportunities Massood Towhidnejad, Embry-Riddle humankind has always for AI, moderated by Ingrid Russell of Aeronautical University. The program felt an overwhelming need to understand the University of Hartford. This group chairs were Bill Manaris and Jim the world around us and to control included an impressive list of panelists: Etheredge, both of the University of it for our own benefit.