262 citations · 1.1k across the 80 of their papers we have counts for
85 papers
What Do Deepfake Audio Detectors Hear? From Self-Supervised Models to Audio LLMs
Wanying Ge, Xin Wang, Junichi Yamagishi
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. W…
Textual Acoustic Grounding for Generalizable LLM-Based Deepfake Voice Detection
Yassine El Kheir, Xin Wang, Wanqing Ge +3
Deepfake voice detection suffers from poor generalization across unseen domains. While Audio Large Language Models (ALLMs) show promise, the modality gap between continuous audio e…
Combining Self-Embedding Audio Watermarking with Ultra-Low-Bitrate Neural Codecs
Yigitcan Özer, Xin Wang, Zhe Zhang +1
Partial manipulation of speech recordings, where only localized segments of an utterance are altered, poses a significant challenge for content integrity verification, as reliable…
A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography
Yigitcan Özer, Zhe Zhang, Wanying Ge +2
Partial deepfake speech, where only limited segments of an utterance are synthesized or manipulated, poses a significant challenge to existing deepfake detection systems. As the pr…
Toward Interpretable Speech Deepfake Detection using Artifact-Specific Experts and Calibrated Detection Scores
Viola Negroni, Xin Wang, Wanying Ge +3
In this work, we propose an interpretable framework for speech deepfake detection based on artifact-specific expert models. Rather than relying on black-box decisions, the framewor…
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai +3
Recent zero-shot text-to-speech (TTS) systems achieve remarkable naturalness and speaker similarity but typically require high-quality speaker prompts and either strip away or enta…