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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Choon Beng Tan
Universiti Malaysia Sabah
Computer Science & IT Engineering

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Artificial speech detection using image-based features and random forest classifier Choon Beng Tan; Mohd Hanafi Ahmad Hijazi; Frazier Kok; Mohd Saberi Mohamad; Puteri Nor Ellyza Nohuddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp161-172

Abstract

The ASVspoof 2015 Challenge was one of the efforts of the research community in the field of speech processing to foster the development of generalized countermeasures against spoofing attacks. However, most countermeasures submitted to the ASVspoof 2015 Challenge failed to detect the S10 attack effectively, the only attack that was generated using the waveform concatenation approach. Hence, more informative features are needed to detect previously unseen spoofing attacks. This paper presents an approach that uses data transformation techniques to engineer image-based features together with random forest classifier to detect artificial speech. The objectives are two-fold: (i) to extract image-based features from the melfrequency cepstral coefficients representation of the speech signal and (ii) to compare the performance of using the extracted features and Random Forest to determine the authenticity of voices with the existing approaches. An audio-to-image transformation technique was used to engineer new features in classifying genuine and spoof voices. An experiment was conducted to find the appropriate combination of the engineered features and classifier. Experimental results showed that the proposed approach was able to detect speech synthesis and voice conversion attacks effectively, with an equal error rate of 0.10% and accuracy of 99.93%.
A comparison of different support vector machine kernels for artificial speech detection Choon Beng Tan; Mohd Hanafi Ahmad Hijazi; Puteri Nor Ellyza Nohuddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24259

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.
Co-Authors Frazier Kok Mohd Hanafi Ahmad Hijazi Mohd Saberi Mohamad Puteri Nor Ellyza Nohuddin