1 citations · 4 across the 15 of their papers we have counts for
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When Semantics Matter: Reliability-Aware Semantic-Rhythm Control for Co-Speech Gesture Generation
Zhirui Xing, Long Ye, Kaige Li +2
Co-speech gesture generation aims to synthesize natural gestures that are both temporally synchronized with speech and semantically consistent with the spoken content. Although rec…
AT-ADD: A Benchmark and Challenge for Robust and All-Type Audio Deepfake Detection
Yuankun Xie, Haonan Cheng, Jiayi Zhou +12
Recent audio generation models can synthesize high-fidelity speech, environmental sound, singing voice, and music, creating new risks for multimedia trust. Existing audio deepfake…
AT-ADD: All-Type Audio Deepfake Detection Challenge Summary
Yuankun Xie, Haonan Cheng, Jiayi Zhou +11
This paper summarizes the ACM Multimedia 2026 AT-ADD Grand Challenge on all-type audio deepfake detection. AT-ADD contains two tracks: robust speech deepfake detection under realis…
AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
Yuankun Xie, Haonan Cheng, Jiayi Zhou +10
The rapid advancement of Audio Large Language Models (ALLMs) has enabled cost-effective, high-fidelity generation and manipulation of both speech and non-speech audio, including so…
Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection
Xiaoxuan Guo, Yuankun Xie, Haonan Cheng +5
Speech deepfake detection (SDD) focuses on identifying whether a given speech signal is genuine or has been synthetically generated. Existing audio large language model (LLM)-based…
Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning
Yuankun Xie, Xiaoxuan Guo, Jiayi Zhou +6
Recent advances in audio large language models (ALLMs) have made high-quality synthetic audio widely accessible, increasing the risk of malicious audio deepfakes across speech, env…