3 papers
cs.SD2026
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…
cs.SD2026
Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Vamshi Nallaguntla, Shruti Kshirsagar, Anderson R. Avila
Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approache…
cs.SD2026
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…