Detecting Converted Speech and Natural Speech for anti-Spoofing Attack in Speaker Recognition

Voice conversion techniques present a threat to speaker verification systems. To enhance the security of speaker verification systems, We study how to automatically distinguish natural speech and synthetic/converted speech. Motivated by the research on phase spectrum in speech perception, in this study, we propose to use features derived from phase spectrum to detect converted speech. The features are tested under three different training situations of the converted speech detector: a) only Gaussian mixture model (GMM) based converted speech data are available; b) only unit-selection based converted speech data are available; c) no converted speech data are available for training converted speech model. Experiments conducted on the National Institute of Standards and Technology (NIST) 2006 speaker recognition evaluation (SRE) corpus show that the performance of the features derived from phase spectrum outperform the mel-frequency cepstral coefficients (MFCCs) tremendously: even without converted speech for training, the equal error rate (EER) is reduced from 20.20% of MFCCs to 2.35%.