UR Channel-robust Synthetic Speech Detection System For Asvspoof 2021
2021 · Xinhui Chen, You Zhang, Ge Zhu, et al.
Abstract
In this paper, we present UR-AIR system submission to the logical access (LA) and the speech deepfake (DF) tracks of the ASVspoof 2021 Challenge. The LA and DF tasks focus on synthetic speech detection (SSD), i.e. detecting text-to-speech and voice conversion as spoofing attacks. Different from previous ASVspoof challenges, the LA task this year presents codec and transmission channel variability, while the new task DF presents general audio compression. Built upon our previous research work on improving the robustness of the SSD systems to channel effects, we propose a channel-robust synthetic speech detection system for the challenge. To mitigate the channel variability issue, we use an acoustic simulator to apply transmission codec, compression codec, and convolutional impulse responses to augmenting the original datasets. For the neural network backbone, we propose to use Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Networks (ECAPA-TDNN) as our primar
Authors
(none)
Tags
Stats
Related papers
- Asasvicomtech: The Vicomtech-ugr Speech Deepfake Detection And SASV Systems For The Asvspoof5 Challenge (2024)5.24
- The DKU Replay Detection System For The Asvspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, And Fusion (2019)12.25
- Exploring Wavlm Back-ends For Speech Spoofing And Deepfake Detection (2024)4.52
- Deep Residual Neural Networks For Audio Spoofing Detection (2019)0.00
- Asvspoof 2021: Towards Spoofed And Deepfake Speech Detection In The Wild (2022)17.95
- AASIST3: Kan-enhanced AASIST Speech Deepfake Detection Using SSL Features And Additional Regularization For The Asvspoof 2024 Challenge (2024)9.03
- An Empirical Study On Channel Effects For Synthetic Voice Spoofing Countermeasure Systems (2021)9.92
- The Vicomtech Audio Deepfake Detection System Based On Wav2vec2 For The 2022 ADD Challenge (2022)14.06