# AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge

arXiv: https://arxiv.org/abs/2408.17352 · HTML: https://mlsanity.com/paper/2408.17352/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks](https://mlsanity.com/paper/2102.11600/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection](https://mlsanity.com/paper/2107.12710/) (2021)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2111.04433/) (2021)
- [STC Antispoofing Systems for the ASVspoof2019 Challenge](https://mlsanity.com/paper/1904.05576/) (2019)
- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [Towards Robust Speech Representation Learning for Thousands of Languages](https://mlsanity.com/paper/2407.00837/) (2024)
- [Deep Residual Neural Networks for Audio Spoofing Detection](https://mlsanity.com/paper/1907.00501/) (2019)
- [Kolmogorov-Arnold Convolutions: Design Principles and Empirical Studies](https://mlsanity.com/paper/2407.01092/) (2024)
- [ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks](https://mlsanity.com/paper/1904.01120/) (2019)
- [SingFake: Singing Voice Deepfake Detection](https://mlsanity.com/paper/2309.07525/) (2023)
- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [LibAUC: A Deep Learning Library for X-Risk Optimization](https://mlsanity.com/paper/2306.03065/) (2023)
- [Improving Short Utterance Anti-Spoofing with AASIST2](https://mlsanity.com/paper/2309.08279/) (2023)
- [AReLU: Attention-based Rectified Linear Unit](https://mlsanity.com/paper/2006.13858/) (2020)
- [UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021](https://mlsanity.com/paper/2107.12018/) (2021)
- [ELA: Efficient Local Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2403.01123/) (2024)
- [Generalized Fake Audio Detection via Deep Stable Learning](https://mlsanity.com/paper/2406.03237/) (2024)

## Built on by (most cited first, 9 of 9)

- [Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning](https://mlsanity.com/paper/2603.10725/) (2026)
- [Discrete Optimal Transport and Voice Conversion](https://mlsanity.com/paper/2505.04382/) (2025)
- [An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures](https://mlsanity.com/paper/2607.03150/) (2026)
- [Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy](https://mlsanity.com/paper/2505.19951/) (2025)
- [Probabilistic Verification of Voice Anti-Spoofing Models](https://mlsanity.com/paper/2603.10713/) (2026)
- [What Do Deepfake Speech Detectors Actually Hear?](https://mlsanity.com/paper/2606.10912/) (2026)
- [SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing](https://mlsanity.com/paper/2606.11674/) (2026)
- [Domain-Adaptive Dual-Gating Mixture of Experts for Generalizable Speech Deepfake Detection](https://mlsanity.com/paper/2609.33709/) (2026)
- [Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors](https://mlsanity.com/paper/2610.16826/) (2026)

## Abstract (arXiv)

Automatic Speaker Verification (ASV) systems, which identify speakers based on their voice characteristics, have numerous applications, such as user authentication in financial transactions, exclusive access control in smart devices, and forensic fraud detection. However, the advancement of deep learning algorithms has enabled the generation of synthetic audio through Text-to-Speech (TTS) and Voice Conversion (VC) systems, exposing ASV systems to potential vulnerabilities. To counteract this, we propose a novel architecture named AASIST3. By enhancing the existing AASIST framework with Kolmogorov-Arnold networks, additional layers, encoders, and pre-emphasis techniques, AASIST3 achieves a more than twofold improvement in performance. It demonstrates minDCF results of 0.5357 in the closed condition and 0.1414 in the open condition, significantly enhancing the detection of synthetic voices and improving ASV security. textbfThe new version of the model is publicly available at hrefhttps://huggingface.co/lab260/Spectra-AASIST3underlineHuggingFace (2026)