TELKOMNIKA Telecommunication Computing Electronics and Control
A comparison of different support vector machine kernels for artificial speech detection

TELKOMNIKA Telecommunication Computing Electronics and Control
A comparison of different support vector machine kernels for artificial speech detection

Artificial speech
Artificial speech detection
Support vector machine

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.

Choon Beng Tan, Mohd Hanafi Ahmad Hijazi, Puteri Nor Ellyza Nohuddin

http://telkomnika.uad.ac.id

Aug 15, 2022

peri irawan

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english

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