40 citations · 68 across the 11 of their papers we have counts for
11 papers
Beyond Speech: Dual-Domain SSL Fusion for Unified All-Type Audio Deepfake Detection
Cunhang Fan, Junqin Cao, Tian Gao +4
Unified all-type audio deepfake detection aims to determine whether an input clip is real or fake when its audio type may be speech, environmental sound, singing voice, or music. E…
RTCFake: Speech Deepfake Detection in Real-Time Communication
Jun Xue, Zhuolin Yi, Yihuan Huang +6
With the rapid advancement of speech generation technologies, the threat posed by speech deepfakes in real-time communication (RTC) scenarios has intensified. However, existing det…
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
Yujie Chen, Jiangyan Yi, Cunhang Fan +10
Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although…
RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection
Yujie Chen, Jiangyan Yi, Jun Xue +7
Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can ef…
Frequency-mix Knowledge Distillation for Fake Speech Detection
Cunhang Fan, Shunbo Dong, Jun Xue +3
In the telephony scenarios, the fake speech detection (FSD) task to combat speech spoofing attacks is challenging. Data augmentation (DA) methods are considered effective means to…
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
Cunhang Fan, Yujie Chen, Jun Xue +3
In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high co…
DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection
Cunhang Fan, Hongyu Zhang, Wei Huang +5
Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in r…
Spatial Reconstructed Local Attention Res2Net with F0 Subband for Fake Speech Detection
Cunhang Fan, Jun Xue, Jianhua Tao +4
The rhythm of bonafide speech is often difficult to replicate, which causes that the fundamental frequency (F0) of synthetic speech is significantly different from that of real spe…
Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection
Shunbo Dong, Jun Xue, Cunhang Fan +3
In this paper, we propose the multi-perspective information fusion (MPIF) Res2Net with random Specmix for fake speech detection (FSD). The main purpose of this system is to improve…
Learning From Yourself: A Self-Distillation Method for Fake Speech Detection
Jun Xue, Cunhang Fan, Jiangyan Yi +4
In this paper, we propose a novel self-distillation method for fake speech detection (FSD), which can significantly improve the performance of FSD without increasing the model comp…
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features
Jun Xue, Cunhang Fan, Zhao Lv +6
Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients,…