Novel Speech Features for Improved Detection of Spoofing Attacks

TL;DR

This paper introduces novel speech features based on frequency-warping and formant-specific transformations to improve the detection of spoofing attacks in speaker verification systems, achieving near-perfect accuracy.

Contribution

It presents new speech features that outperform existing methods in detecting synthetic speech, enhancing anti-spoofing capabilities.

Findings

Proposed features outperform existing approaches in spoofing detection.

Achieved equal error rates (EERs) of 0% in classifying natural and synthetic speech.

Effective against various types of synthetic speech data.

Abstract

Now-a-days, speech-based biometric systems such as automatic speaker verification (ASV) are highly prone to spoofing attacks by an imposture. With recent development in various voice conversion (VC) and speech synthesis (SS) algorithms, these spoofing attacks can pose a serious potential threat to the current state-of-the-art ASV systems. To impede such attacks and enhance the security of the ASV systems, the development of efficient anti-spoofing algorithms is essential that can differentiate synthetic or converted speech from natural or human speech. In this paper, we propose a set of novel speech features for detecting spoofing attacks. The proposed features are computed using alternative frequency-warping technique and formant-specific block transformation of filter bank log energies. We have evaluated existing and proposed features against several kinds of synthetic speech data from ASVspoof 2015 corpora. The results show that the proposed techniques outperform existing approaches for various spoofing attack detection task. The techniques investigated in this paper can also accurately classify natural and synthetic speech as equal error rates (EERs) of 0% have been achieved.