# ODIN112-AI-Assisted Emergency Services in Romania
###### Abstract  
The evolution of Natural Language Processing technologies transformed them into viable choices for various accessibility features and for facilitating interactions between humans and computers. A subset of them consists of speech processing systems, such as Automatic Speech Recognition, which became more accurate and more popular as a result. In this article, we introduce an architecture built around various speech processing systems to enhance Romanian emergency services. Our system is designed to help the operator evaluate various situations with the end goal of reducing the response times of emergency services. We also release the largest high-quality speech dataset of more than 150 h for Romanian. Our architecture includes an Automatic Speech Recognition model to transcribe calls automatically and augment the operator's notes, as well as a Speech Recognition model to classify the caller's emotions. We achieve state-of-the-art results on both tasks, while our demonstrator is designed to be integrated with the Romanian emergency system. Automatic Speech Recognition; Speech Emotion Recognition; Romanian language; emergency services +
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the United States of America or \"999\" in the United Kingdom. The Romanian emergency system follows the European Telecommunications Standards Institute (ETSI) standardization direction of the NG112 (Next Generation 112) specification corresponding to the European emergency services. NG112 brings new functionalities, especially at the level of multimedia communications, which opens the opportunity to implement new types of services. Speech-recognition-based intelligence can ease the work of emergency services dispatchers and provide relevant information for critical cases. Though there are not many AI-enhanced emergency systems in operation, such tools could help analyze calls and messages and support faster decisions. In return, AI tools could reduce waiting times and save more lives by assisting operators in several use cases like filtering abusive calls more efficiently and providing contextualized suggestions to operators in specific situations. An example is the Danish company Corti [1], which developed an AI system to analyze emergency calls and predict cardiac arrests more accurately outside the hospital. According to [PERSON] et al. [2], emergency dispatchers fail to identify about 25% of cases of cardiac arrest and therefore lose the possibility of giving instructions for resuscitation. Artificial Intelligence could detect a cardiac arrest also based on historical data given a predisposition of the caller for such conditions. The developed algorithm listens to the call in real time and alerts the operator if the caller has a cardiac arrest. In 2018, EENA launched a pilot project with Corti to test how this system could work; the project was carried out in various locations in France and Italy. In another implementation, ML2 Grow ([https://ml2 grow.com/case/ai-system-ensures-faster-handling-of-emergency-calls/](https://ml2 grow.com/case/ai-system-ensures-faster-handling-of-emergency-calls/), accessed on 1 December 2022) was used to identify accidental calls. Based on the first seconds of a call, the \"Hazira Digital\" system determined whether the call was accidental or intentional (real). This article presents our solution for the Romanian emergency systems with a focus on the underlying deep models and the overall architecture of the developed system, our methods for data acquisition, and the training of models for a low-resource language.