Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approaches treat speech as a homogeneous signal and largely overlook its internal phonetic structure, limiting their interpretability in emotionally conditioned settings. In this work, we propose a phoneme-level framework to analyze emotionally manipulated synthetic speech using real and EVC-generated speech under matched emotional conditions with shared transcripts, phoneme-aligned TextGrids, and WavLM-based embeddings. Our results show that phoneme behavior varies across categories, with complex vowels and fricatives e...
| Authors | Vamshi Nallaguntla, Shruti Kshirsagar, Anderson R. Avila |
|---|---|
| Primary category | cs.SD |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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