Using Magnitude and Phase Based Spectral Features for Detecting Synthetic and Converted Speech

Year 2020, Ejosat Special Issue 2020 (HORA), 398 - 406, 15.08.2020

Burak Kasapoğlu Turgay Koç

https://doi.org/10.31590/ejosat.780650

Cited By: 2

Abstract

With the advancement in technology, the use of biometric signals that differ from person to person such as fingerprint, retina, face, and voice is becoming more popular in order to provide personal access in applications that need security. The fact that among these biometric signals, voice, that is, speech signal can be easily obtained from the person and provides high mobility make automatic speaker verification (ASV) systems popular. Due to the widespread use of ASV systems in security applications, different spoofing attack methods have been developed to mislead these systems and it is observed that these developed spoofing attack methods pose a serious threat to ASV systems. In this study, a new system is proposed to detect the spoofing attacks using speech synthesis and voice conversion methods, which are two of the biggest threats to ASV systems. Proposed system uses Gaussian Mixture Model based classifier using the fusion of magnitude spectrum based constant Q cepstral coefficients (CQCC), that was chosen as best countermeasure feature of ASVSpoof challenge for detection of speech produced with speech synthesis and voice conversion methods, and glottal flow modified group delay (GFMGD) feature, that contains phase spectrum information of glottal flow obtained by applying inverse filtering on speech signal. In the classification of spoof speech produced by using genuine speech signals, due to both systems having classification error below 1%, it is not found any major difference in classification performance between proposed system and CQCC based baseline system. However, in the classification of spoof speech produced by using waveform filtering method both systems similarly performed poorly compared to other attacking methods. On the other hand, the proposed system can provide up to 55% performance increase against speech signals synthesized or converted by modern artificial neural networks and audio vocoders compared to the baseline system using only CQCC.

Keywords

Speaker Recognition Systems, Speech Processing, Spoofing Attack Detection Systems

Sentetik ve Dönüştürülmüş Konuşmaların Tespitinde Genlik ve Faz Tabanlı Spektral Özniteliklerin Kullanılması

Abstract

Teknolojideki gelişmeyle birlikte güvenlik ihtiyacı bulunan uygulamalarda kişisel erişimi sağlayabilmek amacıyla parmak izi, retina, yüz, ses gibi kişiden kişiye değişiklik gösteren biyometrik sinyallerin kullanımı gün geçtikçe yaygınlaşmaktadır. Bu biyometrik sinyallerden ses yani konuşma sinyalinin hem kişiden kolaylıkla elde edilebilir olması hem de yüksek mobilite sağlaması otomatik konuşmacı doğrulama (Automatic Speaker Verification – ASV) sistemlerini popüler hale getirmektedir. ASV sistemlerinin güvenlik alanlarında yaygınlaşmasıyla birlikte bu sistemleri yanıltmaya yönelik farklı saldırı yöntemleri geliştirilerek bu saldırıların ASV sistemleri için ciddi birer tehdit oluşturduğu gözlenmiştir. Bu çalışmada, ASV sistemlerine en büyük tehdit oluşturan yöntemlerden ikisi olan ses sentezi ve ses dönüştürme yöntemleri kullanılarak ASV sistemlerine yapılan saldırıların tespit edilebilmesi için yeni bir sistem önerilmiştir. Önerilen sistemde, daha önce ses dönüştürme ve ses sentezleme yöntemiyle üretilen sahte seslerin tespit edilebilmesi amacıyla 2015 yılında düzenlenmiş olan ASVSpoof yarışmasında en iyi performansı gösteren genlik spektrumu tabanlı anlık Q kepstral katsayıları (Constant Q Cepstral Coefficients – CQCC) özniteliği ile konuşma sinyalinin ters filtrelenmesiyle elde edilen gırtlak akımına ait faz bilgisi içeren değiştirilmiş grup gecikmesi (Glottal Flow Modified Group Delay – GFMGD) özniteliği birlikte kullanılarak Gauss Karışım Modeli tabanlı sınıflandırma sistemi oluşturulmuştur. Doğrudan gerçek ses parçaları kullanılarak üretilen sahte seslerin sınıflandırılmasında hem CQCC tabanlı temel sistem hem de önerilen sistem için sistem performansları arasında belirgin bir fark görülmeyip her iki sistem de %1’in altında sınıflandırma hatası göstermiştir. Ancak, dalga form filtreleme ile üretilen sahte seslerin sınıflandırılmasında her iki sistem de benzer şekilde diğer saldırı yöntemlerine göre daha zayıf performans göstermiştir. Önerilen sistem, sadece CQCC kullanan temel sistem ile kıyaslandığında özellikle son yıllarda geliştirilmiş olan modern yapay sinir ağları ve ses kodlayıcılar tarafından sentezlenen ya da dönüştürülen konuşma sinyallerine karşı %55’e kadar performans artışı sağlayabilmektedir.

Keywords

Konuşmacı Tanıma Sistemleri, Konuşma İşleme, Sahte Konuşmacı Algılama Sistemleri

Thanks

Bu araştırmada yer alan tüm/kısmi nümerik hesaplamalar TÜBİTAK ULAKBİM, Yüksek Başarım ve Grid Hesaplama Merkezi'nde (TRUBA kaynaklarında) gerçekleştirilmiştir. TÜBİTAK ULAKBİM’e çalışmalarımız sırasında TRUBA kaynaklarını paylaştığı için teşekkür ederiz.

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