Deep neural networks for automatic detection of screams and shouted speech in subway trains

Abstract

Deep Neural Networks (DNNs) have recently become a popular technique for regression and classification problems. Their capacity to learn high-order correlations between input and output data proves to be very powerful for automatic speech recognition. In this paper we investigate the use of DNNs for automatic scream and shouted speech detection, within the framework of surveillance systems in public transportation. We recorded a database of sounds occurring in subway trains in real conditions of exploitation and used DNNs to classify the sounds into screams, shouts and other categories. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.