Anti-spoofing Using Transfer Learning With Variational Information Bottleneck
2022 · Youngsik Eom, Yeonghyeon Lee, Ji Sub Um, et al.
Abstract
Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems. Since such synthetic speech is generated from diverse algorithms, generalization ability with using limited training data is indispensable for a robust anti-spoofing system. In this work, we propose a transfer learning scheme based on the wav2vec 2.0 pretrained model with variational information bottleneck (VIB) for speech anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA) database shows that our method improves the performance of distinguishing unseen spoofed and genuine speech, outperforming current state-of-the-art anti-spoofing systems. Furthermore, we show that the proposed system improves performance in low-resource and cross-dataset settings of anti-spoofing task significantly, demonstrating that our system is also robust in terms of data size and data distribution.
Authors
(none)
Tags
Stats
Related papers
- Automatic Speaker Verification Spoofing And Deepfake Detection Using Wav2vec 2.0 And Data Augmentation (2022)17.35
- Spoofed Training Data For Speech Spoofing Countermeasure Can Be Efficiently Created Using Neural Vocoders (2022)11.93
- Toward Improving Synthetic Audio Spoofing Detection Robustness Via Meta-learning And Disentangled Training With Adversarial Examples (2024)6.77
- One-class Learning Towards Synthetic Voice Spoofing Detection (2020)17.31
- Representation Selective Self-distillation And Wav2vec 2.0 Feature Exploration For Spoof-aware Speaker Verification (2022)9.03
- Spoofing Speaker Verification Systems With Deep Multi-speaker Text-to-speech Synthesis (2019)0.00
- Experimental Study: Enhancing Voice Spoofing Detection Models With Wav2vec 2.0 (2024)0.00
- Deep Residual Neural Networks For Audio Spoofing Detection (2019)0.00