Non-linguistic vocal event detection using online random forest

Accurate detection of non-linguistic vocal events in social signals can have a great impact on the applicability of speech enabled interactive systems. In this paper, we investigate the use of random forest for vocal event detection. Random forest technique has been successfully employed in many areas such as object detection, face recognition, and audio event detection. This paper proposes to use online random forest technique for detecting laughter and filler and for analyzing the importance of various features for non-linguistic vocal event classification through permutation. The results show that according to the Area Under Curve measure the online random forest achieved 88.1% compared to 82.9% obtained by the baseline support vector machines for laughter classification and 86.8% to 83.6% for filler classification.