A Unique Glottal Flow Parameters based Features for Anti-spoofing Countermeasures in Automatic Speaker Verification

Abstract

The domain of Automatic Speaker Verification (ASV) is blooming with growing developments in feature engineering and artificial intelligence. Inspite of this, the system is liable to spoofing attacks in the form of synthetic or replayed speech. The difficulty in detecting synthetic speech is due to recent advancements in the Voice conversion and Text-to-speech systems which produce natural, indistinguishable speech. To prevent such attacks, there is a need to develop robust spoof detection systems. In order to achieve this goal, we are proposing estimation of Glottal Flow Parameters (GFP) from speech of genuine speech and synthetic spoof samples. The GFP are further parameterized using time, frequency and Liljencrants-Fant (LF) models. Along with GFP features, the Linear Prediction Cepstrum Co-efficient (LFCC) and statistical parameters are computed. The GFP features are investigated to prove their usefulness in detecting spoofed and genuine speech. The ASV spoof 2019 corpus is used to test the framework and evaluated against the baseline models. The proposed spoof detection framework produces an Equal Error Rate (EER) of 2.39% and tandem Detection Cost Function (t-DCF) of 0.0562 which is found to be better than the state-of-the art technique.

An improved normalized gain-based score normalization technique for spoof detection algorithm

“…During the training phase, the feature extraction represents the enrolled samples for various types of attacks along with the genuine speech utterances. The commonly used renowned features for spoof detection in ASV framework are Linear Prediction Residual [6], Glottal Flow parameters (GFP) [7], CQCC [3], Line Frequency Cepstral Coefficient (LFCC) [3], Phase based features like Modified Group Delay (MGD) [8] and Deep features [9]. These features have shown significant improvement in the EER.…”