SocialSense: A Collaborative Mobile Platform for Speaker and Mood Identification

We present SocialSense, a collaborative smartphone based speaker and mood identification and reporting system that uses a user's voice to detect and log his/her speaking and mood episodes. SocialSense collaboratively works with other phones that are running the app present in the vicinity to periodically send/receive speaking and mood vectors to/from other users present in a social interaction setting, thus keeping track of the global speaking episodes of all users with their mood. In addition, it utilizes a novel event-adaptive dynamic classification scheme for speaker identification which updates the speaker classification model every time one or more users enter or leave the scenario, ensuring a most updated classifier based on user presence. Evaluation of using dynamic classifiers shows that SocialSense improves speaker identification accuracy by 30% compared to traditional static speaker identification systems, and a 10% to 43% performance boost under various noisy environments. SocialSense also improves the mood classification accuracy by 4% to 20% compared to the baseline approaches. Energy consumption experiments show that its device daily lifetime is between 10-14 hours.