40 citations · 128 across the 28 of their papers we have counts for
16 papers · 1 filter
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…
Dual-Branch Knowledge Distillation for Noise-Robust Synthetic Speech Detection
Cunhang Fan, Mingming Ding, Jianhua Tao +4
Most research in synthetic speech detection (SSD) focuses on improving performance on standard noise-free datasets. However, in actual situations, noise interference is usually pre…
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…
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
Jiangyan Yi, Chenglong Wang, Jianhua Tao +5
Many datasets have been designed to further the development of fake audio detection. However, fake utterances in previous datasets are mostly generated by altering timbre, prosody,…
Fully Automated End-to-End Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +6
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However,…
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,…
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Jiangyan Yi, Ruibo Fu, Jianhua Tao +17
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios.…
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
Cunhang Fan, Bin Liu, Jianhua Tao +3
Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much…
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition
Cunhang Fan, Jiangyan Yi, Jianhua Tao +3
The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR). However,…
Dynamic Attention Based Generative Adversarial Network with Phase Post-Processing for Speech Enhancement
Andong Li, Chengshi Zheng, Renhua Peng +2
The generative adversarial networks (GANs) have facilitated the development of speech enhancement recently. Nevertheless, the performance advantage is still limited when compared w…
A Recursive Network with Dynamic Attention for Monaural Speech Enhancement
Andong Li, Chengshi Zheng, Cunhang Fan +2
A person tends to generate dynamic attention towards speech under complicated environments. Based on this phenomenon, we propose a framework combining dynamic attention and recursi…
Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features
Cunhang Fan, Bin Liu, Jianhua Tao +2
Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speaker-independent speech separation. DC is usually formulated…