A Speech Emotion Recognition Model to Detect Aggressive Behavior in Dialogues

Abstract

Speech Emotion Recognition (SER) is a multidisciplinary field that involves the development of computational models to automatically detect and analyze emotional states conveyed through speech signals. Utilizing techniques from signal processing, machine learning, and natural language processing, SER systems extract relevant features from audio data and classify emotions into distinct categories such as happiness, sadness, anger, and more. This work aims to leverage the latest SER techniques to build a robust model that can detect aggressive behavior in dialogues solely based on audio input signals.