"Deep Learning Approaches for Sarcasm Detection in Audio Signals: A Literature Review"

This study reviews recent progress in sarcasm detection, with a particular emphasis on audio-based methods. Drawing on 58 scholarly articles, it traces the development of machine learning, deep learning, and hybrid approaches designed to identify sarcasm through vocal features such as intonation, pitch, and rhythm. The review underscores the need for robust models capable of capturing cultural and linguistic variations in how sarcasm is conveyed. Looking ahead, researchers are encouraged to explore multimodal systems that combine audio with textual analysis to boost accuracy. The broader significance of this work lies in its potential to enhance human-computer interaction and communication technologies across diverse sectors worldwide.