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20242026
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cs.SD2026

DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer

Yisu Liu, Chenxing Li, Wanqian Zhang +6

Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…

cs.SD2025

M3-TTS: Multi-modal DiT Alignment & Mel-latent for Zero-shot High-fidelity Speech Synthesis

Xiaopeng Wang, Chunyu Qiang, Ruibo Fu +10

Non-autoregressive (NAR) text-to-speech synthesis relies on length alignment between text sequences and audio representations, constraining naturalness and expressiveness. Existing…

cs.SD2025

RPRA-ADD: Forgery Trace Enhancement-Driven Audio Deepfake Detection

Ruibo Fu, Xiaopeng Wang, Zhengqi Wen +8

Existing methods for deepfake audio detection have demonstrated some effectiveness. However, they still face challenges in generalizing to new forgery techniques and evolving attac…

cs.SD2025

P2Mark: Plug-and-play Parameter-level Watermarking for Neural Speech Generation

Yong Ren, Jiangyan Yi, Tao Wang +7

Neural speech generation (NSG) has rapidly advanced as a key component of artificial intelligence-generated content, enabling the generation of high-quality, highly realistic speec…

cs.SD2025

Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition

Yuankun Xie, Xiaopeng Wang, Zhiyong Wang +7

Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task. However, ex…

cs.SD2024

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

Hongming Guo, Ruibo Fu, Yizhong Geng +9

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face…