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

TL;DR

This paper proposes an end-to-end deep learning approach using wavelet-based layers for detecting doctored speech, significantly improving spoof detection accuracy over traditional features and baseline models.

Contribution

It introduces wavelet scattering and continuous wavelet transform layers into deep neural networks for speech spoof detection, with a novel wavelet deconvolution layer for optimized feature extraction.

Findings

Wavelet-based models outperform traditional handcrafted features.

The Wavelet Deconvolution layer improves model performance.

Significant relative improvements on ASVspoof 2019 dataset.

Abstract

The Automatic Speaker Verification systems have potential in biometrics applications for logical control access and authentication. A lot of things happen to be at stake if the ASV system is compromised. The preliminary work presents a comparative analysis of the wavelet and MFCC-based state-of-the-art spoof detection techniques developed in these papers, respectively (Novoselov et al., 2016) (Alam et al., 2016a). The results on ASVspoof 2015 justify our inclination towards wavelet-based features instead of MFCC features. The experiments on the ASVspoof 2019 database show the lack of credibility of the traditional handcrafted features and give us more reason to progress towards using end-to-end deep neural networks and more recent techniques. We use Sincnet architecture as our baseline. We get E2E deep learning models, which we call WSTnet and CWTnet, respectively, by replacing the Sinc layer with the Wavelet Scattering and Continuous wavelet transform layers. The fusion model achieved 62% and 17% relative improvement over traditional handcrafted models and our Sincnet baseline when evaluated on the modern spoofing attacks in ASVspoof 2019. The final scale distribution and the number of scales used in CWTnet are far from optimal for the task at hand. So to solve this problem, we replaced the CWT layer with a Wavelet Deconvolution(WD) (Khan and Yener, 2018) layer in our CWTnet architecture. This layer calculates the Discrete-Continuous Wavelet Transform similar to the CWTnet but also optimizes the scale parameter using back-propagation. The WDnet model achieved 26% and 7% relative improvement over CWTnet and Sincnet models respectively when evaluated over ASVspoof 2019 dataset. This shows that more generalized features are extracted as compared to the features extracted by CWTnet as only the most important and relevant frequency regions are focused upon.