Work

UDA-SD: Unsupervised domain adaptation based on self-distillation for cross-domain audio anti-spoofing

Year: 2025

Type: article

Abstract: Audio anti-spoofing algorithms perform well in in-domain scenarios, but their performance deteriorates significantly under domain mismatch conditions. Some studies have incorporated domain adversarial training strategy to encourage the learning of domain-invariant representations, reducing domain differences. However, this strategy could compromise the model’s ability to distinguish between bona fide and spoofed audio, limiting its effectiveness in mitigating domain mismatch. To address this problem, we propose a novel U nsupervised D omain A daptation algorithm based on S elf- D istillation, termed UDA-SD. We introduce a Siamese framework with two branches tailored for the source and target domains, including three key modules: (i) The source branch leverages pre-trained audio representations to enhance spoofing discrimination capability, encouraging the model to capture the common differences between bona fide and spoofed audio. (ii) The target branch captures domain-specific and intrinsic features through a self-distillation module, thereby reducing domain discrepancies. (iii) Within the self-distillation module, we align the student model’s predictions for varying-duration audio segments with the teacher model’s predictions for long-duration segments. This design enhances the feature exploration of target samples by capturing both global and local representations, uncovering comprehensive differences between categories. To evaluate the effectiveness of the proposed UDA-SD, we conducted eight cross-domain anti-spoofing tasks. Experimental results showed the effectiveness of our approach with respect to source-only audio anti-spoofing models, achieving a relative 54.31 % to 85.14 % performance improvement in cross-domain scenarios.

Source: Applied Soft Computing

DOI: 10.1016/j.asoc.2025.114044

Authors: Ruiteng Zhang, Xiaohuan Chen, Wenhuan Lu, Xugang Lu, Junhai Xu, Jianguo Wei

Institutions: Tianjin University, National Institute of Information and Communications Technology, Qinghai University for Nationalities

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,730

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China