Effectiveness of Mel Scale-Based ESA-IFCC Features for Classification of Natural vs. Spoofed Speech

The performance of biometric systems based on Automatic Speaker Verification (ASV) degrades due to spoofing attacks, generated using different speech synthesis (SS) and voice conversion (VC) techniques. Results of recent ASV spoof 2015 challenge indicate that spoof-aware features are a possible solution, rather than focusing on a powerful classifier. In this paper, we investigate the effect of various frequency scales (such as, ERB, Mel and linear) applied on a Gabor filterbank. The output of filterbank was used to exploit the contribution of instantaneous frequency (IF) in each subband energy via Teager Energy Operator-based Energy Separation Algorithm (TEO-ESA) to capture possible changes in spectral envelope of spoofed speech. The IF is computed from narrowband components of the speech signal and Discrete Cosine Transform (DCT) is applied on deviations in IF, which are referred to as Instantaneous Frequency Cosine Coefficients (IFCC). The classification results on static features shows an EER of 1.32% with Mel frequency scale and 1.87% with linear. The results with delta feature of linear frequency scale gets reduced further to 1.39% whereas, with Mel scale, it increased by 0.64% on development set of ASV spoof 2015 challenge database.