Classifiers for Synthetic Speech Detection: A Comparison
Küçük Resim Yok
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Isca-Int Speech Communication Assoc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 speaker recognition to detect synthetic speech. Experimental results conducted on the ASVspoof 2015 dataset show that support vector 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.
Açıklama
16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015) -- SEP 06-10, 2015 -- Dresden, GERMANY
Sahidullah, Md/0000-0002-0624-2903
Sahidullah, Md/0000-0002-0624-2903
Anahtar Kelimeler
spoof detection, countermeasures, speaker recognition
Kaynak
16Th Annual Conference Of The International Speech Communication Association (Interspeech 2015), Vols 1-5
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
