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
Citations
IRE Journals:
Asfiya Khanum , Soubiya Siddiqua
"Emotional Deepfake Detection Via Voice Stress Analysis" Iconic Research And Engineering Journals Volume 9 Issue 4 2025 Page 890-893
IEEE:
Asfiya Khanum , Soubiya Siddiqua
"Emotional Deepfake Detection Via Voice Stress Analysis" Iconic Research And Engineering Journals, 9(4)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12