Modelling Glottal Flow Derivative Signal for Detection of Replay Speech Samples

Abstract

It is a widely known fact that automatic speaker verification systems are quite vulnerable to replay speech. The present work deals with detecting replay speech by using the information available in glottal flow derivative (GFD) signal. In signal processing terms, the speech signal can be represented as the response of a vocal-tract system with excited by a excitation source in the form of glottal flow. The effect of record and replay devices distorted the spectral characteristics of the naturally uttered speech sample, resulting distortion in corresponding GFD signals. In this work the GFD signals are parameterized by using standard mel filters and Gaussian mixtures models are made for detection. Although various methods are available, by correlation analysis it is observed that in the context of the present work the dynamic programming phase slope algorithm (DYPSA) method is relatively more effective in estimating the GFD signals. The experimental studies are made on ASVSpoof2017 database. The proposed glottal flow derivative mel frequency cepstral coefficients (GFDMFCC) feature provides 20.53% equal error rate (EER). This performance is comparatively poor than by speech and residual based features. It is mainly due to the absence of fine structure information in estimated GFD signal. However, in fusion with speech signal based constant-Q cepstral coefficients (CQCC) features, the GFDMFCC feature provides an improvement of 10.30% with reference to conventional residual feature. This shows the usefulness of modelling GFD signals for detection of replay signals.