A methodology for emergency calls severity prediction: from pre-processing to BERT-based classifiers

International audienceEmergency 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 pre-process 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

Ontology Population Framework of MAGNETO for Instantiating Heterogeneous Forensic Data Modalities

© 2019, IFIP International Federation for Information Processing. The growth in digital technologies has influenced three characteristics of information namely the volume, the modality and the frequency. As the amount of information generated by individuals increases, there is a critical need for the Law Enforcement Agencies to exploit all available resources to effectively carry out criminal investigation. Addressing the increasing challenges in handling the large amount of diversified media modalities generated at high-frequency, the paper outlines a systematic approach adopted for the processing and extraction of semantic concepts formalized to assist criminal investigations. The novelty of the proposed framework relies on the semantic processing of heterogeneous data sources including audio-visual footage, speech-to-text, text mining, suspect tracking and identification using distinctive region or pattern. Information extraction from textual data, machine-translated into English from various European languages, uses semantic role labeling. All extracted information is stored in one unifying system based on an ontology developed specifically for this task. The described technologies will be implemented in the Multimedia Analysis and correlation enGine for orgaNised crime prEvention and invesTigatiOn (MAGNETO)