Automatic stress detection in emergency (telephone) calls

Abstract

The abundance of calls to emergency lines during crises is difficult to handle by the limited number of operators. Detecting if the caller is experiencing some extreme emotions can be a solution for distinguishing the more urgent calls. Apart from these, there are several other applications that can benefit from awareness of the emotional state of the speaker. This paper describes the design of a system for selecting the calls that appear to be urgent, based on emotion detection. The system is trained using a database of spontaneous emotional speech from a call-centre. Four machine learning techniques are applied, based on either prosodic or spectral features, resulting in individual detectors. As a last stage, we investigate the effect of fusing these detectors into a single detection system. We observe an improvement in the equal error rate (EER) from 19.0% on average for four individual detectors to 4.2% when fused using linear logistic regression. All experiments are performed in a speaker independent cross-validation framework.