Real-world audio recordings for deepfake detection rarely consist solely of speech; they often are mixtures of speech and background events that themselves may also be synthetic. When processing these mixtures, it remains unclear whether detectors attend to the speech or the background event, and where their spectral e...
This paper revisits audio steganography from a new perspective and proposes a self-embedding strategy in which a clean speech signal embeds a compressed representation of itself, enabling post-hoc extraction of reference content.
Yigitcan Özer, Zhe Zhang, Wan-Ying Ge et al.· 1 citation
DAIEN-TTS is presented, an environment-aware zero-shot TTS framework that disentangles and jointly models speech, background noise, and reverberation, enabling independent control over timbre and acoustic environment through separate speaker and environment prompts.
Deepfake voice detection suffers from poor generalization across unseen domains. While Audio Large Language Models (ALLMs) show promise, the modality gap between continuous audio embeddings which capture the subtle acoustic details necessary for deepfake detection and the semantic space of LLMs remains a critical, unde...
Y. El Kheir, Xin Wang, Wan-Ying Ge et al.· 1 citation
Experiments across four controlled manipulation types under ideal channel conditions show that the embedded payload, and hence an approximate reconstruction of the authentic content, is always fully recovered without bit errors, and the results indicate that the choice of neural codec is the dominant factor for detecti...
Yigitcan Özer, Xin Wang, Zhe Zhang et al.· 0 citations
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