| system: |
| name: "AASIST" |
| slug: "aasist" |
| description: > |
| AASIST: audio anti-spoofing using integrated spectro-temporal graph |
| attention networks. Sinc-convolution front-end, RawNet2-style residual |
| encoder, and heterogeneous stacking graph attention over spectral and |
| temporal sub-graphs with a learnable readout. Official clovaai/aasist |
| ASVspoof2019 LA pretrained checkpoint, FP32, deterministic |
| first-64600-sample window (no random crop). |
| code: "https://github.com/clovaai/aasist" |
| checkpoint: "https://huggingface.co/SpeechAntiSpoofingBenchmarks/AASIST/blob/e842653505c2832ac9f46bbf56173b0f54ef82a7/AASIST.pth" |
| params_millions: 0.297866 |
| paper: |
| arxiv_id: "2110.01200" |
| url: "https://arxiv.org/abs/2110.01200" |
| bibtex: | |
| @inproceedings{jung2022aasist, |
| title={{AASIST}: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks}, |
| author={Jung, Jee-weon and Heo, Hee-Soo and Tak, Hemlata and Shim, Hye-jin and Chung, Joon Son and Lee, Bong-Jin and Yu, Ha-Jin and Evans, Nicholas}, |
| booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| pages={6367--6371}, |
| year={2022}, |
| organization={IEEE} |
| } |
| notes: > |
| Official AASIST variant only (not AASIST-L). Deterministic first-64600-sample |
| window (no random crop), matching clovaai/aasist data_utils.pad() used at eval. |
| Checkpoint mirrored to SpeechAntiSpoofingBenchmarks/AASIST (pinned). |
| |
