Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection

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Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection
Title:
Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection
Journal Title:
IEEE/ACM Transactions on Audio, Speech, and Language Processing
Publication Date:
17 April 2023
Citation:
Xu, L., Yang, J., You, C. H., Qian, X., & Huang, D. (2023). Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1574–1586. https://doi.org/10.1109/taslp.2023.3267610
Abstract:
Replay speech poses a growing threat to speaker verification systems, thus the detection of replay speech becomes increasingly important. A critical factor differentiating replay speech and genuine speech is the representation of device information. Replay speech carries physical device information that originates from recording device, playback device, and environmental noise. In this work, a device-related linear transformation strategy is proposed to disentangle non-device information from replay speech. First, we conduct factor analysis by introducing a common vector for both replay utterance and the corresponding genuine speech utterance on parallel training data; then, we derive an expectation maximization formula to obtain the parameters of the device related linear transformation; subsequently, three device feature extraction methods are developed based on the device-related linear transformation. The developed device features are evaluated on ASVspoof 2017 version 2.0 and ASVspoof 2021 physical access corpora. The experimental results demonstrate that our proposed linear transformation strategy is effective for replay spoofing detection, and the resultant device features outperform many typical features. Moreover, our spoofing detection systems display superior performance over several competitive state-of-the-art systems.
License type:
Publisher Copyright
Funding Info:
This work was supported in part by the National Natural Science Foundation of China under Grant 62001100
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2329-9304
2329-9290
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