Automatic Sensing of Speech Activity and Correlation with Mood Changes

he association between social relationships and psychological health has been established fairly recently, in the last 30-40 years, relying on survey-based methods to record past activities and the psychological responses in individuals. However, using the self-reporting methods for capturing social behavior exhibits a number of shortcomings including recall bias, memory dependence, and a high end user effort for a continuous long-term monitoring. In contrast, automated sensing techniques for monitoring social activity, and in general, human behavior, has a potential to provide more objective measurements thus to overcome the shortcomings of self-reporting methods. In this paper, we present a privacy preserving approach to detect one component of social interactions - the speech activity, through the use of off-the-shelf accelerometers. Furthermore, we used the accelerometer based speech detection method to investigate the correlation between the amount of speech (which is an aspect that reflects the participation in verbal social interactions) and mood changes. Our pilot study suggested that verbal interactions are an important factor that has an impact on individuals’ mood, while the study also demonstrated the potential of automated capturing social activity comparable to the use of gold standard surveys

Predicting continuous conflict perception with Bayesian Gaussian processes

Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception