Active Learning for Speech Event Detection in HCI

In this work, a pool-based active learning approach combining outlier detection methods with uncertainty sampling is proposed for speech event detection. Events in this case are regarded as atypical utterances (e.g. laughter, heavy breathing) occurring sporadically during a Human Computer Interaction (HCI) scenario. The proposed approach consists in using rank aggregation to select informative speech segments which have previously been ranked using different outlier detection techniques combined with an uncertainty sampling technique. The uncertainty sampling method is based on the distance to the boundary of a Support Vector Machine with Radial Basis Function kernel trained on the available annotated samples. Extensive experimental results prove the effectiveness of the proposed approach.