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Speech synthesis and voice conversion techniques can pose threats to current speaker verification (SV) systems. For this purpose, it is essential to develop front end systems that are able to distinguish human speech vs. spoofed speech (synthesized or voice converted). In this paper, for the ASVspoof 2015 challenge, we propose a detector based on(More)
Most of the state-of-the-art voice biometrics systems use the natural speech signal (either read speech or spontaneous or contextual speech) from the subjects. In this paper, an attempt is made to identify speakers from their hum. A new feature set, viz., Variable length Teager Energy Based Mel Frequency Cepstral Coefficients (VTMFCC) is proposed for this(More)
In this paper, use of Viterbi-based algorithm and spectral transition measure (STM)-based algorithm for the task of speech data labeling is being attempted. In the STM framework, we propose use of several spectral features such as recently proposed cochlear filter cepstral coefficients (CFCC), perceptual linear prediction cepstral coefficients (PLPCC) and(More)