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This paper deals with the problem of detecting replay attacks on speaker verification systems. In literature, apart from the acoustic features, source features have also been successfully used for this task. In existing source features, only the information around glottal closure instants (GCIs) have been utilized. We hypothesize that the feature derived by capturing the temporal dynamics between two GCIs would be more discriminative for such task. Motivated by that, in this work we explore the use of discrete cosine transform compressed integrated linear prediction residual (ILPR) features for discriminating between genuine and replayed signals. A spoof detection system is built using the compressed ILPR feature and a Gaussian mixture model (GMM) classifier. A baseline system is also built using constant-Q cepstral coefficient feature with GMM backend. These systems are tested on the ASVSpoof 2017 Version 2.0 database.

Added on May 9, 2019


  More Details
  • Contributed by : Individual
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Sarfaraz Jelil, Sishir Kalita, S. R. Mahadeva ,Rohit Sinha