A Methodology for Emergency Calls Severity Prediction: From Pre-processing to BERT-Based Classifiers

Abstract

Emergency call centers are often required to properly assess and prioritise emergency situations pre-intervention, in order to provide the required assistance to the callers efficiently. In this paper, we present an end-to-end pipeline for emergency calls analysis. Such a tool can be found useful as it is possible for the intervention team to misinterpret the severity of the situation or mis-prioritise callers. The data used throughout this work is one week's worth of emergency call recordings provided by the French SDIS 25 firemen station, located in the Doubs. We preprocess the calls and evaluate several artificial intelligence models in the classification of callers' situation as either severe or non-severe. We demonstrate through our results that it is possible, with the right selection of algorithms, to predict if the call will result in a serious injury with a 71% accuracy, based on the caller's speech only. This shows that it is indeed possible to assist emergency centers with an autonomous tool that is capable of analysing the caller's description of their situation and assigning an appropriate priority to their call.