# UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021

arXiv: https://arxiv.org/abs/2107.12018 · HTML: https://mlsanity.com/paper/2107.12018/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection](https://mlsanity.com/paper/2406.10283/) (2024)
- [AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge](https://mlsanity.com/paper/2408.17352/) (2024)
- [HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods](https://mlsanity.com/paper/2309.08208/) (2023)
- [Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion](https://mlsanity.com/paper/2302.12434/) (2023)
- [Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification](https://mlsanity.com/paper/2203.17031/) (2022)
- [Deep Spectro-temporal Artifacts for Detecting Synthesized Speech](https://mlsanity.com/paper/2210.05254/) (2022)
- [Generalizable Audio Spoofing Detection using Non-Semantic Representations](https://mlsanity.com/paper/2509.00186/) (2025)
- [GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2609.35411/) (2026)

## Abstract (arXiv)

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 primary model. We also incorporate one-class learning with channel-robust training strategies to further learn a channel-invariant speech representation. Our submission achieved EER 20.33% in the DF task; EER 5.46% and min-tDCF 0.3094 in the LA task.