Artificial speech detection using image-based features and random forest classifier
The ASV spoof 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 ASV spoof 2015 Challenge failed to detect the S10 attack e...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Intelektual Pustaka Media Utama (IPMU)
2022
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/32707/1/Artificial%20speech%20detection%20using%20image.pdf https://eprints.ums.edu.my/id/eprint/32707/2/Artificial%20speech%20detection%20using%20image1.pdf https://eprints.ums.edu.my/id/eprint/32707/ https://ijai.iaescore.com/index.php/IJAI/article/view/21201 http://doi.org/10.11591/ijai.v11.i1.pp161-172 |
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| Summary: | The ASV spoof 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 ASV spoof 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 Mel frequency 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%. |
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