4 papers · 1 filter
Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection
Zihan Pan, Sailor Hardik, Jinyang Wu
Large speech foundation models have shown strong potential for speech deepfake detection, but direct fine-tuning is limited by a mismatch between self-supervised pre-training objec…
Quantizer-Aware Hierarchical Neural Codec Modeling for Speech Deepfake Detection
Jinyang Wu, Zihan Pan, Qiquan Zhang +2
Neural audio codecs discretize speech via residual vector quantization (RVQ), forming a coarse-to-fine hierarchy across quantizers. While codec models have been explored for repres…
SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
Jinyang Wu, Nana Hou, Zihan Pan +3
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet current datasets cover SEA languages only sparsely, leaving models…
MoLEx: Mixture of LoRA Experts in Speech Self-Supervised Models for Audio Deepfake Detection
Zihan Pan, Sailor Hardik Bhupendra, Jinyang Wu
While self-supervised learning (SSL)-based models have boosted audio deepfake detection accuracy, fully finetuning them is computationally expensive. To address this, we propose a…