Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics

• URL: http://arxiv.org/abs/2009.01934v2
• Date: Sun, 11 Apr 2021 11:41:43 GMT
• Title: Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics
• Authors: Arun Kumar Singh (1), Priyanka Singh (2) ((1) Indian Institute of Technology Jammu, (2) Dhirubhai Ambani Institute of Information and Communication Technology)
• Abstract summary: We propose an approach to distinguish human speech from AI synthesized speech. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital technology has made possible unimaginable applications come true. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. We integrate both these analyses and propose a machine learning model to detect AI synthesized speech.

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