Штучний інтелект

Анотація: This paper presents a parameter-efficient approach to detecting AI-generated voices under domain shift. The task is formulated as binary classification of bona fide and spoofed speech. A frozen WavLM Base+ backbone is used as the feature encoder, while only a lightweight post-encoder adapter and a linear classifier are trained. The study follows a strict source-only protocol: models are trained on ASVspoof 2019 LA train, selected on ASVspoof 2019 LA development data, and evaluated on ASVspoof 2021 LA and the external In-the-Wild dataset. The main comparison includes no adaptation, an MLP adapter, and a KAN-inspired adapter under nearly identical trainable-parameter budgets. Experimental results show that both learned adapters substantially improve cross-domain performance compared with the no-adaptation baseline. The KAN-inspired adapter achieves the lowest mean EER on ASVspoof 2021 LA, while MLP and KAN-inspired adaptation remain close on In-the-Wild. The results indicate that lightweight post-encoder adaptation is effective for improving transfer under benchmark domain shift, whereas the advantage of KAN-inspired nonlinear adaptation is domain-dependent rather than universal.

Ключові слова: audio deepfake detection, speech anti-spoofing, domain shift, parameter-efficient adaptation, WavLM, KAN-inspired adapter, equal error rate

Посилання:

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