Document Type
Article
Publication Date
2025
DOI
10.3390/info16030194
Publication Title
Information
Volume
16
Issue
3
Pages
194 (1-14)
Abstract
Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability to perceive local features through residual convolutional blocks and squeeze-and-excitation (SE) mechanisms. Additionally, cross-adaptable weights (CAWs) are used to guide the model in focusing on task-relevant shallow information, thereby enabling the effective fusion of features from different layers of the pre-trained model. Experimental results show that the proposed method achieved an equal error rate (EER) of 0.36% and 4.29% on the ASVspoof2019 logical access (LA) and ASVspoof2021 LA datasets, respectively, demonstrating excellent detection performance and generalization ability.
Rights
© 2025 by the authors
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
ORCID
0000-0003-2695-9642 (Zheng)
Original Publication Citation
Yuan, H., Zhang, L., Niu, B., & Zheng, X. (2025). A spoofing speech detection method combining multi-scale features and cross-layer information. Information, 16(3), 1-14, Article 194. https://doi.org/10.3390/info16030194
Repository Citation
Yuan, Hongyan; Zhang, Linjuan; Niu, Baoning; and Zheng, Xianrong, "A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Identification" (2025). Information Technology & Decision Sciences Faculty Publications. 112.
https://digitalcommons.odu.edu/itds_facpubs/112
Included in
Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Technology and Innovation Commons
Comments
Data availability statement: Article states: "Data are contained within the article."