Automatic stress detection in emergency (telephone) calls

Iulia Lefter · Léon J. M. Rothkrantz · David A. van Leeuwen · Pascal Wiggers

48 citations0/0 files storedInternational Journal of Intelligent Defence Support Systems

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

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.

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References

2002Praat, a system for doing phonetics by computer 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2006Emotional speech recognition: Resources, features, and methods 2003Toward an affect-sensitive multimodal human-computer interaction 2003Emotional speech: Towards a new generation of databases 2002Vocal Expression Of Emotion 2001Nonlinear feature based classification of speech under stress 2008The New Handbook of Methods in Nonverbal Behavior Research

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2015Person-specific behavioural features for automatic stress detection 2022SERAB: A Multi-Lingual Benchmark for Speech Emotion Recognition 2010Emotion Recognition from Speech by Combining Databases and Fusion of Classifiers

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