Add spectra-aasist3 submission for EmoFake_test (EER 0.29%, reproduced: scoring)

submissions/spectra-aasist3.yaml

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+schema_version: 4
+system:
+  name: Spectra-AASIST3
+  slug: spectra-aasist3
+  description: 'Speech deepfake detector: a wav2vec 2.0 XLS-R-300m self-supervised
+    front-end feeding a single-layer MLP bridge (1024->128) into a KAN-enhanced AASIST
+    (KAN-AASIST) 2-class classifier with four-branch spectro-temporal graph attention
+    and a KAN output layer. Pre-release lab260/Spectra-AASIST3 checkpoint. FP32, preemphasis
+    (0.97), deterministic first-64600-sample window (no random crop). score = output
+    logit for class 1 (bona fide). Unpublished / pre-release model (no paper).
+
+    '
+  code: https://huggingface.co/lab260/Spectra-AASIST3
+  checkpoint: https://huggingface.co/lab260/Spectra-AASIST3
+  params_millions: 318.9489
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/EmoFake_test
+  revision: d822eced42093f7c2ce77195049490260b3956d2
+  split: test
+scores:
+  eer_percent: 0.2857142857142857
+  n_trials: 17500
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/lab260/Spectra-AASIST3/resolve/3c42a43c9b02fd0941aa5f60cf29df7d6fbbf434/.eval_results/SpeechAntiSpoofingBenchmarks/EmoFake_test/scores.txt
+  scores_sha256: 931e0a66d8fdff8ae486f94b6c844bdcaeb6d857eefc903629deea50a99d56b8
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-24'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-24'
+notes: 'Spectra-AASIST3 (wav2vec2 XLS-R-300m SSL front-end -> MLP bridge -> KAN-AASIST)
+  from lab260/Spectra-AASIST3 (model.safetensors, self-contained incl. the SSL encoder).
+  Loaded via PyTorchModelHubMixin.from_pretrained; the XLS-R-300m base is built from
+  facebook/wav2vec2-xls-r-300m then every weight is overwritten by the checkpoint.
+  Preemphasis (0.97) applied to the full waveform before a deterministic first-64600-sample
+  window, matching the source README eval pipeline. score = logit for class 1 (bona
+  fide); higher = more bona fide (README: index 0 = spoof, index 1 = bonafide; classify()
+  thresholds logits[:, 1]). Unpublished / pre-release -> no paper -> Unpublished/Proprietary
+  tier (unranked).
+
+  '