Datasets:
submissions: add whisper-mfcc-mesonet
Browse files
submissions/whisper-mfcc-mesonet.yaml
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schema_version: 4
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system:
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name: Whisper-MFCC-MesoNet
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slug: whisper-mfcc-mesonet
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description: '(Whisper + MFCC) MesoNet anti-spoofing countermeasure: a Whisper tiny.en
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audio encoder front-end concatenated (2 channels) with an MFCC+Δ+ΔΔ front-end,
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feeding a MesoInception4 classifier. Whisper encoder fine-tuned end-to-end. FP32.
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Upstream eval pipeline reproduced: sox silence-trim (silence 1 0.2 1% -1 0.2 1%)
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then a 30 s (480000-sample) repeat-pad window. This is the best MesoNet configuration
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from the paper.
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'
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code: https://github.com/piotrkawa/deepfake-whisper-features
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checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet
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paper:
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arxiv_id: '2306.01428'
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url: https://arxiv.org/abs/2306.01428
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bibtex: "@inproceedings{kawa23b_interspeech,\n title = {Improved DeepFake\
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\ Detection Using Whisper Features},\n author = {Piotr Kawa and Marcin Plata\
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\ and Micha{\\l} Czuba and Piotr Szyma{\\'n}ski and Piotr Syga},\n year \
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\ = {2023},\n booktitle = {Proc. INTERSPEECH 2023},\n pages = {4009--4013},\n\
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\ doi = {10.21437/Interspeech.2023-1537},\n}\n"
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params_millions: 7.660881
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dataset:
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id: SpeechAntiSpoofingBenchmarks/CFAD
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revision: 0713bfc413326170ec52cfdb4ae9cbd546d38f7a
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split: test
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scores:
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eer_percent: 31.934854040668604
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n_trials: 62999
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n_skipped: 0
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artifact:
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scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet/resolve/8dac50082c1b98b0c2825c61d507b67a3744c236/.eval_results/SpeechAntiSpoofingBenchmarks/CFAD/scores.txt
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scores_sha256: 6a62d2d8a63e51052b1f40454709670abd3c6fdfc3089a5eb6154978fa06a774
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bench_version: speech-spoof-bench==0.3.4
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reproduction: {}
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submitter:
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hf_username: korallll
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contact: k.n.borodin@mtuci.ru
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submitted_at: '2026-06-08'
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notes: 'Fine-tuned Whisper+MFCC MesoNet (best MesoNet config from the paper). Reproduces
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the paper''s In-the-Wild EER (26.72%) to within 0.01 pp with the upstream sox silence-trim
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+ 30 s repeat-pad preprocessing.
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'
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