# Audio Deep Fake Detection System with Neural Stitching for ADD 2022

arXiv: https://arxiv.org/abs/2204.08720 · HTML: https://mlsanity.com/paper/2204.08720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 3 of 3)

- [Cross-Domain Audio Deepfake Detection: Dataset and Analysis](https://mlsanity.com/paper/2404.04904/) (2024)
- [Dual-Branch Knowledge Distillation for Noise-Robust Synthetic Speech Detection](https://mlsanity.com/paper/2310.08869/) (2023)
- [DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection](https://mlsanity.com/paper/2506.05851/) (2025)

## Abstract (arXiv)

This paper describes our best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection ChallengeciteYi2022ADD. The very same system was used for both two rounds of evaluation in Track 3.2 with a similar training methodology. The first round of Track 3.2 data is generated from Text-to-Speech(TTS) or voice conversion (VC) algorithms, while the second round of data consists of generated fake audio from other participants in Track 3.1, aiming to spoof our systems. Our systems use a standard 34-layer ResNet, with multi-head attention pooling citeindia2019self to learn the discriminative embedding for fake audio and spoof detection. We further utilize neural stitching to boost the model's generalization capability in order to perform equally well in different tasks, and more details will be explained in the following sessions. The experiments show that our proposed method outperforms all other systems with a 10.1% equal error rate(EER) in Track 3.2.