5 papers
SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization
Yuan Tseng, Aishwarya Fursule, Andrew Zijun Ma +4
Recent improvements in voice-cloning speech generation systems raise concerns about misuse by malicious actors to impersonate others and spread misinformation. Detecting such tampe…
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar +1
Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demogra…
Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains critically underexamined. Prio…
PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake Detection and Naturalness Evaluation
Vamshi Nallaguntla, Aishwarya Fursule, Shruti Kshirsagar +1
The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion…
Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biomet…