Classifiers for synthetic speech detection: a comparison
Cemal Hanilçi, Tomi Kinnunen, Md Sahidullah, А. С. Сизов · 2015
Automatic speaker verification (ASV) systems are highly vulnerable against spoofing attacks, also known as imposture. With recent developments in speech synthesis and voice conversion technology, it has become important to detect synthesized or voice-converted speech for the security of ASV systems. In this paper, we compare five different classifiers used in speakerrecognition to detect synthetic speech. Experimental results conducted on the ASVspoof 2015 dataset show that supportvector machines with generalized linear discriminant kernel(GLDS-SVM) yield the best performance on the development set with the EER of 0.12 % whereas Gaussian mixture model (GMM) trained using maximum likelihood (ML) criterion with the EER of 3.01 % is superior for the evaluation set.