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Emotional Deepfake Detection Via Voice Stress Analysis
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Cybersecurity
Abstract
The rapid advancement of generative artificial intelligence has enabled the creation of highly convincing audio deepfakes, where synthetic voices can mimic real speakers with near-human accuracy Posing new threats in fraud, misinformation, and security. Current detection techniques largely rely on acoustic artifacts or signal irregularities, which are increasingly difficult to identify as synthesis models improve. This paper introduces a novel approach for emotional deepfake detection via voice stress analysis. By examining subtle stress and emotion-related cues?such as pitch fluctuations, jitter, shimmer, rhythm, and speech rate? we capture inconsistencies that synthetic voices struggle to replicate. Using emotional speech datasets alongside AI-generated voice samples, we train deep learning models to distinguish authentic from synthetic speech. Results highlight stress-based analysis as a promising defense against evolving deepfake audio attacks
How to cite this paper
@article{1711237,
author = {Asfiya Khanum, Soubiya Siddiqua},
title = {Emotional Deepfake Detection Via Voice Stress Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {4},
pages = {890-893},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711237.pdf},
abstract = {The rapid advancement of generative artificial intelligence has enabled the creation of highly convincing audio deepfakes, where synthetic voices can mimic real speakers with near-human accuracy Posing new threats in fraud, misinformation, and security. Current detection techniques largely rely on acoustic artifacts or signal irregularities, which are increasingly difficult to identify as synthesis models improve. This paper introduces a novel approach for emotional deepfake detection via voice stress analysis. By examining subtle stress and emotion-related cues?such as pitch fluctuations, jitter, shimmer, rhythm, and speech rate? we capture inconsistencies that synthetic voices struggle to replicate. Using emotional speech datasets alongside AI-generated voice samples, we train deep learning models to distinguish authentic from synthetic speech. Results highlight stress-based analysis as a promising defense against evolving deepfake audio attacks},
month = {October},
}