201 citations · 374 across the 45 of their papers we have counts for
8 papers · 1 filter
WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection
Xi Xuan, Xuechen Liu, Wenxin Zhang +3
Modern front-end design for speech deepfake detection relies on full fine-tuning of large pre-trained models like XLSR. However, this approach is not parameter-efficient and may le…
Can External Sources Help the Knowledge Cut-off Issues in Audio Deepfake Detection?
Xuechen Liu, Xin Wang, Junichi Yamagishi +1
In conventional deepfake audio detection pipelines, inference and evaluation are typically conducted using standalone, parameterized models trained exclusively on a fixed dataset.…
SMoE: An Algorithm-System Co-Design for Pushing MoE to the Edge via Expert Substitution
Guoying Zhu, Meng Li, Haipeng Dai +6
The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts per query. Deploying MoE on consum…
LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech
Xuechen Liu, Wanying Ge, Xin Wang +1
This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio…
From Sharpness to Better Generalization for Speech Deepfake Detection
Wen Huang, Xuechen Liu, Xin Wang +2
Generalization remains a critical challenge in speech deepfake detection (SDD). While various approaches aim to improve robustness, generalization is typically assessed through per…
Post-training for Deepfake Speech Detection
Wanying Ge, Xin Wang, Xuechen Liu +1
We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-sp…
Quantifying Source Speaker Leakage in One-to-One Voice Conversion
Scott Wellington, Xuechen Liu, Junichi Yamagishi
Using a multi-accented corpus of parallel utterances for use with commercial speech devices, we present a case study to show that it is possible to quantify a degree of confidence…
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
Xin Wang, Héctor Delgado, Hemlata Tak +26
ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce…