AI-Enhanced Emergency Call Center Assistant and Situational Awareness for Optimized Emergency Response: Using Speech Recognition, NLP Mapping, NER

This paper presents an AI-powered assistant specifically designed for emergency call centers to enhance the speed and accuracy of emergency response processes. The system uses cutting-edge models like Whisper for real-time transcription of audio, BERT for extracting critical information using Named Entity Recognition (NER), and Wav2Vec2 for emotional cue detection in speech. By recording important details—such as the caller’s location, type of emergency, and emotional state—the assistant reduces dispatcher workload and improves prioritization in emergency situations. The formalized information is processed through Natural Language Processing (NLP) algorithms and geocoded through Google Maps APIs to generate real-time situational awareness maps. The models were tested and assessed using datasets such as RAVDESS and their performance was determined using typical measures such as Word Error Rate (WER), F1-score, and accuracy. The solution outlined in this work shows significant gains in response effectiveness, information fidelity, and situational awareness and provides a scalable framework for updating emergency communication systems.