Synthetic speech detection using phase information

Taking advantage of the fact that most of the speech processing techniques neglect the phase information, we seek to detect phase perturbations in order to prevent synthetic impostors attacking Speaker Verification systems. Two Synthetic Speech Detection (SSD) systems that use spectral phase related information are reviewed and evaluated in this work: one based on the Modified Group Delay (MGD), and the other based on the Relative Phase Shift, (RPS). A classical module-based MFCC system is also used as baseline. Different training strategies are proposed and evaluated using both real spoofing samples and copy-synthesized signals from the natural ones, aiming to alleviate the issue of getting real data to train the systems. The recently published ASVSpoof2015 database is used for training and evaluation. Performance with completely unrelated data is also checked using synthetic speech from the Blizzard Challenge as evaluation material. The results prove that phase information can be successfully used for the SSD task even with unknown attacks.This work has been partially supported by the Basque Government (ElkarOla Project, KK-2015/00,098) and the Spanish Ministry of Economy and Competitiveness (Restore project, TEC2015-67,163-C2-1-R)

Use of the harmonic phase in synthetic speech detection

Special Session paper: recent PhD thesis descriptionThis PhD dissertation was written by Jon Sanchez and supervised by Inma Hernáez and Ibon Saratxaga. It was defended at the University of the Basque Country the 5th of February 2016. The committee members were Dr. Alfonso Ortega Giménez (UniZar), Dr. Daniel Erro Eslava (UPV/EHU) and Dr. Enric Monte Moreno (UPC). The dissertation was awarded a "sobresaliente cum laude” qualification.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with FEDER support (RESTORE project,TEC2015-67163-C2-1-R) and the Basque Government (ELKAROLA project, KK-2015/00098)

Use of the harmonic phase in synthetic speech detection

Special Session paper: recent PhD thesis descriptionThis PhD dissertation was written by Jon Sanchez and supervised by Inma Hernáez and Ibon Saratxaga. It was defended at the University of the Basque Country the 5th of February 2016. The committee members were Dr. Alfonso Ortega Giménez (UniZar), Dr. Daniel Erro Eslava (UPV/EHU) and Dr. Enric Monte Moreno (UPC). The dissertation was awarded a "sobresaliente cum laude” qualification.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with FEDER support (RESTORE project,TEC2015-67163-C2-1-R) and the Basque Government (ELKAROLA project, KK-2015/00098)

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

A spoof detection algorithm supports the speaker verification system to examine the false claims by an imposter through careful analysis of input test speech. The scores are employed to categorize the genuine and spoofed samples effectively. Under the mismatch conditions, the false acceptance ratio increases and can be reduced by appropriate score normalization techniques. In this article, we are using the normalized Discounted Cumulative Gain (nDCG) norm derived from ranking the speaker’s log-likelihood scores. The proposed scoring technique smoothens the decaying process due to logarithm with an added advantage from the ranking. The baseline spoof detection system employs Constant Q-Cepstral Co-efficient (CQCC) as the base features with a Gaussian Mixture Model (GMM) based classifier. The scores are computed using the ASVspoof 2019 dataset for normalized and without normalization conditions. The baseline techniques including the Zero normalization (Z-norm) and Test normalization (T-norm) are also considered. The proposed technique is found to perform better in terms of improved Equal Error Rate (EER) of 0.35 as against 0.43 for baseline system (no normalization) wrt to synthetic attacks using development data. Similarly, improvements are seen in the case of replay attack with EER of 7.83 for nDCG-norm and 9.87 with no normalization (no-norm). Furthermore, the tandem-Detection Cost Function (t-DCF) scores for synthetic attack are 0.015 for no-norm and 0.010 for proposed normalization. Additionally, for the replay attack the t-DCF scores are 0.195 for no-norm and 0.17 proposed normalization. The system performance is satisfactory when evaluated using evaluation data with EER of 8.96 for nDCG-norm as against 9.57 with no-norm for synthetic attacks while the EER of 9.79 for nDCG-norm as against 11.04 with no-norm for replay attacks. Supporting the EER, the t-DCF for nDCG-norm is 0.1989 and for no-norm is 0.2636 for synthetic attacks; while in case of replay attacks, the t-DCF is 0.2284 for the nDCG-norm and 0.2454 for no-norm. The proposed scoring technique is found to increase spoof detection accuracy and overall accuracy of speaker verification system