Evince the artifacts of Spoof Speech by blending Vocal Tract and Voice Source Features

TL;DR

This paper introduces a system that detects spoof speech and identifies the generating algorithm by analyzing Vocal Tract and Voice Source features, achieving high accuracy and providing insights into speech artifacts.

Contribution

It proposes a novel approach combining VTS and VS features for spoof detection and algorithm classification, enhancing robustness through model fusion.

Findings

VS features focus on phoneme transitions

VTS features emphasize stationary speech segments

Fusion improves overall classification robustness

Abstract

With the rapid advancement in synthetic speech generation technologies, great interest in differentiating spoof speech from the natural speech is emerging in the research community. The identification of these synthetic signals is a difficult task not only for the cutting-edge classification models but also for humans themselves. To prevent potential adverse effects, it becomes crucial to detect spoof signals. From a forensics perspective, it is also important to predict the algorithm which generated them to identify the forger. This needs an understanding of the underlying attributes of spoof signals which serve as a signature for the synthesizer. This study emphasizes the segments of speech signals critical in identifying their authenticity by utilizing the Vocal Tract System(\textit{VTS}) and Voice Source(\textit{VS}) features. In this paper, we propose a system that detects spoof signals as well as identifies the corresponding speech-generating algorithm. We achieve 99.58\% in algorithm classification accuracy. From experiments, we found that a VS feature-based system gives more attention to the transition of phonemes, while, a VTS feature-based system gives more attention to stationary segments of speech signals. We perform model fusion techniques on the VS-based and VTS-based systems to exploit the complementary information to develop a robust classifier. Upon analyzing the confusion plots we found that WaveRNN is poorly classified depicting more naturalness. On the other hand, we identified that synthesizer like Waveform Concatenation, and Neural Source Filter is classified with the highest accuracy. Practical implications of this work can aid researchers from both forensics (leverage artifacts) and the speech communities (mitigate artifacts).