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This work introduces AffectDF, the most comprehensive benchmark for emotionally expressive speech deepfakes, spanning TTS, VC, emotional VC, and LALM-based spoofing attacks across both acted and spontaneous emotional speech.
A replay-regularized, attack-aware curriculum that steps exposure based on measured attack influence is proposed that shows improved overall robustness and reduced attack-level imbalance compared with standard multi-attack training.
This work presents the first fine-grained cross-lingual analysis of prosody using multilingual dubbing data across English-German, English-Spanish, and English-French language pairs and reveals inherent cross-lingual correlations in prosodic structure between certain languages.
DiffAnon is proposed, a diffusion-based anonymization method with classifier-free guidance (CFG) that provides explicit, continuous inference-time control over prosody preservation, and is the first voice anonymization framework to provide structured, interpolatable inference-time prosody control.
This work revisits this design choice and proposes a sub-center modeling framework for speaker embeddings, which improves intelligibility, increases pitch variability, achieves higher naturalness ratings, and retains strong speaker verification performance in zero-shot voice conversion.
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