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Computer Science Colloquia

Monday, October 6, 2014
Mohsin Y. Ahmed
Adviser: Jack Stankovic
Attending Faculty: Alf Weaver (Chair), Yanjun "Jane" Qi and Gabriel Robins

Rice Hall, Rm. 504 at 1:00 PM

PhD Qualifying Exam Presentation
SocialSense: A Collaborative Mobile Platform for Speaker and Mood Identification

ABSTRACT
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 also 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.