Speaker Siglet Detection for Business Microscope
Jun Nishimura, Nobuo Sato, Tadahiro Kuroda · 2008
"Business Microscope" is our sensornet application in the age of knowledge, which visualizes knowledge workers' interactions by sensing their face-to-face communications. Due to the limitation of energy consumption of sensor nodes and privacy concerns, very short (0.1s) intermittently sensed (10s interval) noise-like signals called siglet is used to for detection task. To detect the speaker from the limited input, "self" vs "others" classification problem is introduced. For this new classification problem, new classifier called AdaBoost LVQ is studied to explore the application of AdaBoost to reduce the error rate of the conventional classifier with strictly limited inputs. As a result, AdaBoost LVQ achieved highest recognition accuracy of 96.45% with 19.86% error rate improvement relative to best conventional classifier.