Semantic Indexing of Video Simulations for Enhancing Medical Care During Crises

We propose a machine learning based speaker segmentation and identification system that provides the physician with automated tools to segment, semantically index and retrieve specific segments from a large database of medical simulation video sessions. Instead of working directly in the original feature space, our approach maps low-level audio features to more meaningful histogram descriptors using possibilistic membership functions. The parameters of the membership functions are learned from representative training data. Using 4 medical simulation videos, we show that our approach outperforms existing speaker identification algorithms using standard MFCC and PLP speech features. The proposed speaker identification algorithm was integrated in a GUI to facilitate video retrieval and analysis. Using this GUI, the physician can efficiently identify who spoke and when. In addition, our system can extract useful statistics and features in a completely unsupervised way.