We describe the initial development stage of a robot companion that can assist the communication and detection of emotions in interactions where some modalities are totally or partially compromised. Such is the case for people living with Parkinson's disease. Our approach is based on a Latent Dirichlet Allocation topic model as a principled way to extract features from speech based on a trained classifier that can be linked to measures of emotion. The trained model is integrated into a robotic cognitive architecture to perform real-time, continuous speech detection of positive, negative, or neutral emotional valence that is expressed through the facial features demonstrated of a humanoid robot. To evaluate the integrated system, we conducted a human-robot interaction experiment in which the robot credibly detected and displayed emotions as it listened to utterances spoken by a confederate.
@article{valenti19at,
title={Using topic modeling to infer the emotional state of people living with Parkinson's disease},
author={Andrew P. Valenti and Meia Chita-Tegmark and Linda Tickle-Degnen and Alexander W. Bock and Matthias J. Scheutz},
year={2019},
journal={Assistive Technology},
publisher={Taylor and Francis},
pages={1--10}
url={https://hrilab.tufts.edu/publications/valenti19at.pdf}
}