6 papers
Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning
Xi Xuan, Wenxin Zhang, Zhiyu Li +3
Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting so…
WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection
Xi Xuan, Davide Carbone, Wenxin Zhang +2
Existing front-ends for speech deepfake detection are primarily categorized into two types. Hand-crafted filterbank features are transparent but limited in capturing higher-level i…
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…
Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Xi Xuan, Zimo Zhu, Wenxin Zhang +2
Advances in speech synthesis intensify security threats, motivating real-time deepfake detection research. We investigate whether bidirectional Mamba can serve as a competitive alt…
Multilingual Source Tracing of Speech Deepfakes: A First Benchmark
Xi Xuan, Yang Xiao, Rohan Kumar Das +1
Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applic…
PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement
Zizhen Lin, Junyu Wang, Ruili Li +2
Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive pe…