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The Automatic Speaker Verification systems have potential in biometricsapplications for logical control access and authentication. A lot of thingshappen to be at stake if the ASV system is compromised. The preliminary workpresents a comparative analysis of the wavelet and MFCC-based state-of-the-artspoof detection techniques developed in these papers, respectively (Novoselovet al., 2016) (Alam et al., 2016a). The results on ASVspoof 2015 justify ourinclination towards wavelet-based features instead of MFCC features. Theexperiments on the ASVspoof 2019 database show the lack of credibility of thetraditional handcrafted features and give us more reason to progress towardsusing end-to-end deep neural networks and more recent techniques. We useSincnet architecture as our baseline. We get E2E deep learning models, which wecall WSTnet and CWTnet, respectively, by replacing the Sinc layer with theWavelet Scattering and Continuous wavelet transform layers. The fusion modelachieved 62% and 17% relative improvement over traditional handcrafted modelsand our Sincnet baseline when evaluated on the modern spoofing attacks inASVspoof 2019.  The final scale distribution and the number of scales used in CWTnet are farfrom optimal for the task at hand. So to solve this problem, we replaced theCWT layer with a Wavelet Deconvolution(WD) (Khan and Yener, 2018) layer in ourCWTnet architecture. This layer calculates the Discrete-Continuous WaveletTransform similar to the CWTnet but also optimizes the scale parameter usingback-propagation. The WDnet model achieved 26% and 7% relative improvement overCWTnet and Sincnet models respectively when evaluated over ASVspoof 2019dataset. This shows that more generalized features are extracted as compared tothe features extracted by CWTnet as only the most important and relevantfrequency regions are focused upon.

Detection of Doctored Speech: Towards an End-to-End Parametric Learn-able Filter Approach