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- Title
A comparison of different support vector machine kernels for artificial speech detection.
- Authors
Choon Beng Tan; Hijazi, Mohd Hanafi Ahmad; Nohuddin, Puteri Nor Ellyza
- Abstract
As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
- Subjects
SUPPORT vector machines; SPEECH synthesis; SPEECH; ERROR rates; PUBLIC key cryptography
- Publication
Telkomnika, 2023, Vol 21, Issue 1, p97
- ISSN
1693-6930
- Publication type
Article
- DOI
10.12928/TELKOMNIKA.v21i1.24259