Evince The Artifacts Of Spoof Speech By Blending Vocal Tract And Voice Source Features
2022 · Tadipatri Uday Kiran Reddy, Sahukari Chaitanya Varun, Kota Pranav Kumar Sankala Sreekanth, et al.
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
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