6 papers
WST-X Series: Wavelet Scattering Transform for Interpretable Speech Deepfake Detection
Xi Xuan, Davide Carbone, Wenxin Zhang +2
In this work, we focus on front-end design for speech deepfake detectors, the component that determines the discriminative acoustic cues provided to the classifier. Existing approa…
Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning
Xi Xuan, Wenxin Zhang, Zhiyu Li +4
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…
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…