# Detection of AI-Synthesized Speech Using Cepstral & Bispectral Statistics

arXiv: https://arxiv.org/abs/2009.01934 · HTML: https://mlsanity.com/paper/2009.01934/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection](https://mlsanity.com/paper/2210.02746/) (2022)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition](https://mlsanity.com/paper/2107.11412/) (2021)
- [Segment Transformer: AI-Generated Music Detection via Music Structural Analysis](https://mlsanity.com/paper/2509.08283/) (2025)
- [Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space](https://mlsanity.com/paper/2409.12745/) (2024)
- [Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection](https://mlsanity.com/paper/2606.15117/) (2026)

## Abstract (arXiv)

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.