Extended Constant-Q Cepstral Coefficients for Detection of Spoofing Attacks

Abstract

The constant-Q cepstral coefficients (CQCC) feature is one of the most effective feature in the field of spoof speech detection. The extraction of this feature involves constant-Q transform that captures long range information from the signal. It is followed by uniform resampling of the octave power spectrum to have linear power spectrum from which the CQCC features are obtained. However, we hypothesize that the information obtained from octave power spectrum is complementary with that captured by the linear spectrum. In this regard, we propose to combine the coefficients generated using both linear and octave power spectrum. The combined feature is referred to as extended CQCC (eCQCC) which is hypothesized to have better discriminative information for detection of spoof attacks. The studies for spoof detection are conducted on both synthetic voice and replay based spoofing attacks using ASVspoof 2015 and ASVspoof 2017 Version 2.0 database, respectively. The studies confirm that the proposed eCQCC feature consistently outperforms the baseline CQCC feature in all tasks.