Design of spoof speech detection system teager energy based approach

Abstract

Automatic Speaker Verification (ASV) systems are vulnerable to various spoofing attacks, namely, Speech Synthesis (SS), Voice Conversion (VC), Replay, and Impersonation. The study of spoofing countermeasures has become increasingly important and is currently a critical area of research, which is the principal objective of this thesis. With the development of Neural Network-based techniques, in particular, for machine generated spoof speech signals, the performance of Spoof Speech Detection (SSD) system will be further challenging. To encourage the development of countermeasures that are based on signal processing techniques or neural network-based features for SSD task, a standardized dataset was provided by the organizers of ASVspoof challenge campaigns during 2015, 2017, and 2019. The front-end features extracted from the speech signal has a huge impact in the field of signal processing applications. The goal of feature extraction is to estimate the meaningful information directly from the speech signal that can be helpful to the pattern classifier, speech, speaker, emotion recognition, etc. Among various spoofing attacks, speech synthesis, voice conversion, and replay attacks have been identified as the most effective and accessible forms of spoofing. Accordingly, this thesis investigates and develops a framework to extract the discriminative features to deflect these three spoofing attacks. The main contribution of the thesis is to propose various feature sets as frontend countermeasures for SSD task using a traditional Gaussian Mixture Model (GMM)-based classification system. The feature sets are based on Teager Energy Operator (TEO) and Energy Separation Algorithm (ESA), namely, Teager Energy Cepstral Coefficients (TECC), Energy Separation Algorithm Instantaneous Frequency Cepstral Coefficients (ESA-IFCC), Energy Separation Algorithm Instantaneous Amplitude Cepstral Coefficients (ESA-IACC), Amplitude Weighted Frequency Cepstral Coefficients (AWFCC), Gabor Teager Filterbank (GTFB). The motivation behind...