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

Authors

Ms. Reetu Awasthi

Department of Electronics and Computer science, RTMNU, Nagpur (India)

Dr. Vinay Chavan

Seth Kesarimal Porwal College of Arts and Science and Commerce, Kamptee (India)

Article Information

DOI: 10.51584/IJRIAS.2025.100900068

Subject Category: Education

Volume/Issue: 10/9 | Page No: 676-689

Publication Timeline

Submitted: 2025-09-17

Accepted: 2025-09-24

Published: 2025-10-17

Abstract

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.

Keywords

Sarcasm detection, Machine learning, Audio analysis.

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