Automatic Speaker Verification and Replay Attack Detection System using novel Glottal Flow Cepstrum Coefficients

Abstract

With the growing number of devices that use voice biometric system for verification purposes, it becomes mandatory to accurately verify the target speaker. Moreover, intruders use different spoofing attacks to fool an automatic speaker verification (ASV) system. Voice replay attack being the easiest to generate among other spoofing attacks is frequently employed in front of the ASV systems of these devices to get access to the system for malicious purposes. Thus, we need to develop robust systems that are capable of classifying the target speaker and differentiate between the bonafide and replay voice. To combat the vulnerabilities of ASV systems, we proposed an automatic speaker verification and replay attack detection (ASVRAD) system. For this purpose, we proposed a novel audio feature i.e., Glottal Flow Gammatone Cepstrum Coefficients (GLGTCC), an enhanced version of Glottal Flow Cepstrum Coefficients (GLFCC) for accurate classification of the bonafide speakers and voice replay attacks detection. Our GLGTCC features can reliably capture the speaker-specific attributes from their vocal excitation patterns in the audio. Later, we employed a multiclass support vector machine (SVM) to classify the bonafide speakers, and a binary SVM for classification of bonafide and replay audios. Experimental results of our system on the standard ASVspoof 2019 PA dataset achieved the lowest EER of 3.00% for speaker verification and 18.38% on voice replay attacks over the comparative methods.