A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection

• URL: http://arxiv.org/abs/2603.01482v1
• Date: Mon, 02 Mar 2026 05:45:55 GMT
• Title: A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection
• Authors: Hashim Ali, Nithin Sai Adupa, Surya Subramani, Hafiz Malik,
• Abstract summary: Spoof-SUPERB is a benchmark for audio deepfake detection.<n>We evaluate 20 SSL models spanning generative, discriminative, and spectrogram-based architectures.
• Score: 2.432576583937997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has transformed speech processing, with benchmarks such as SUPERB establishing fair comparisons across diverse downstream tasks. Despite it's security-critical importance, Audio deepfake detection has remained outside these efforts. In this work, we introduce Spoof-SUPERB, a benchmark for audio deepfake detection that systematically evaluates 20 SSL models spanning generative, discriminative, and spectrogram-based architectures. We evaluated these models on multiple in-domain and out-of-domain datasets. Our results reveal that large-scale discriminative models such as XLS-R, UniSpeech-SAT, and WavLM Large consistently outperform other models, benefiting from multilingual pretraining, speaker-aware objectives, and model scale. We further analyze the robustness of these models under acoustic degradations, showing that generative approaches degrade sharply, while discriminative models remain resilient. This benchmark establishes a reproducible baseline and provides practical insights into which SSL representations are most reliable for securing speech systems against audio deepfakes.

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