85 citations · 410 across the 77 of their papers we have counts for
16 papers · 2 filters
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
Xiaohui Zhang, Jiangyan Yi, Chenglong Wang +3
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective…
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…
Fewer-token Neural Speech Codec with Time-invariant Codes
Yong Ren, Tao Wang, Jiangyan Yi +4
Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech code…
Residual Speaker Representation for One-Shot Voice Conversion
Le Xu, Jiangyan Yi, Tao Wang +4
Recently, there have been significant advancements in voice conversion, resulting in high-quality performance. However, there are still two critical challenges in this field. First…
Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms
Chu Yuan Zhang, Jiangyan Yi, Jianhua Tao +2
Recent strides in neural speech synthesis technologies, while enjoying widespread applications, have nonetheless introduced a series of challenges, spurring interest in the defence…
Audio Deepfake Detection: A Survey
Jiangyan Yi, Chenglong Wang, Jianhua Tao +3
Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the prob…
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
Xiaohui Zhang, Jiangyan Yi, Jianhua Tao +2
Current fake audio detection algorithms have achieved promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a…
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…
TST: Time-Sparse Transducer for Automatic Speech Recognition
Xiaohui Zhang, Mangui Liang, Zhengkun Tian +2
End-to-end model, especially Recurrent Neural Network Transducer (RNN-T), has achieved great success in speech recognition. However, transducer requires a great memory footprint an…
Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Xiaohui Zhang +3
Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-l…
Adaptive Fake Audio Detection with Low-Rank Model Squeezing
Xiaohui Zhang, Jiangyan Yi, Jianhua Tao +3
The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches,…
ADD 2023: the Second Audio Deepfake Detection Challenge
Jiangyan Yi, Jianhua Tao, Ruibo Fu +15
Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the…
TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +4
Current fake audio detection relies on hand-crafted features, which lose information during extraction. To overcome this, recent studies use direct feature extraction from raw audi…
Detection of Cross-Dataset Fake Audio Based on Prosodic and Pronunciation Features
Chenglong Wang, Jiangyan Yi, Jianhua Tao +3
Existing fake audio detection systems perform well in in-domain testing, but still face many challenges in out-of-domain testing. This is due to the mismatch between the training a…
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…
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion
Haogeng Liu, Tao Wang, Ruibo Fu +3
Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality. Due to their sim…