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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:	Tan, Choon Beng, Mohd Hanafi Ahmad Hijazi, Frazier Kok, Mohd Saberi Mohamad, Puteri Nor Ellyza Nohuddin
格式:	Article
语言:	English English
出版:	Intelektual Pustaka Media Utama (IPMU) 2022
主题:	QA75.5-76.95 Electronic computers. Computer science
在线阅读:	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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id	my.ums.eprints.32707
record_format	eprints
spelling	my.ums.eprints.327072022-06-09T00:41:07Z https://eprints.ums.edu.my/id/eprint/32707/ Artificial speech detection using image-based features and random forest classifier Tan, Choon Beng Mohd Hanafi Ahmad Hijazi Frazier Kok Mohd Saberi Mohamad Puteri Nor Ellyza Nohuddin QA75.5-76.95 Electronic computers. Computer science 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%. Intelektual Pustaka Media Utama (IPMU) 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32707/1/Artificial%20speech%20detection%20using%20image.pdf text en https://eprints.ums.edu.my/id/eprint/32707/2/Artificial%20speech%20detection%20using%20image1.pdf Tan, Choon Beng and Mohd Hanafi Ahmad Hijazi and Frazier Kok and Mohd Saberi Mohamad and Puteri Nor Ellyza Nohuddin (2022) Artificial speech detection using image-based features and random forest classifier. IAES International Journal of Artificial Intelligence (IJ-AI), 11 (1). pp. 161-172. ISSN 2252-8938 https://ijai.iaescore.com/index.php/IJAI/article/view/21201 http://doi.org/10.11591/ijai.v11.i1.pp161-172
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English English
topic	QA75.5-76.95 Electronic computers. Computer science
spellingShingle	QA75.5-76.95 Electronic computers. Computer science Tan, Choon Beng Mohd Hanafi Ahmad Hijazi Frazier Kok Mohd Saberi Mohamad Puteri Nor Ellyza Nohuddin Artificial speech detection using image-based features and random forest classifier
description	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%.
format	Article
author	Tan, Choon Beng Mohd Hanafi Ahmad Hijazi Frazier Kok Mohd Saberi Mohamad Puteri Nor Ellyza Nohuddin
author_facet	Tan, Choon Beng Mohd Hanafi Ahmad Hijazi Frazier Kok Mohd Saberi Mohamad Puteri Nor Ellyza Nohuddin
author_sort	Tan, Choon Beng
title	Artificial speech detection using image-based features and random forest classifier
title_short	Artificial speech detection using image-based features and random forest classifier
title_full	Artificial speech detection using image-based features and random forest classifier
title_fullStr	Artificial speech detection using image-based features and random forest classifier
title_full_unstemmed	Artificial speech detection using image-based features and random forest classifier
title_sort	artificial speech detection using image-based features and random forest classifier
publisher	Intelektual Pustaka Media Utama (IPMU)
publishDate	2022
url	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
_version_	1760231063188144128
score	13.251813

Artificial speech detection using image-based features and random forest classifier

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