# Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges

arXiv: https://arxiv.org/abs/2507.00324 · HTML: https://mlsanity.com/paper/2507.00324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System](https://mlsanity.com/paper/2508.20983/) (2025)
- [LJ-Spoof: A Generatively Varied Corpus for Audio Anti-Spoofing and Synthesis Source Tracing](https://mlsanity.com/paper/2601.07958/) (2026)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)

## Abstract (arXiv)

Recent advances in speech synthesis have introduced unprecedented challenges in maintaining voice authenticity, particularly concerning public figures who are frequent targets of impersonation attacks. This paper presents a comprehensive methodology for collecting, curating, and generating synthetic speech data for political figures and a detailed analysis of challenges encountered. We introduce a systematic approach incorporating an automated pipeline for collecting high-quality bonafide speech samples, featuring transcription-based segmentation that significantly improves synthetic speech quality. We experimented with various synthesis approaches; from single-speaker to zero-shot synthesis, and documented the evolution of our methodology. The resulting dataset comprises bonafide and synthetic speech samples from ten public figures, demonstrating superior quality with a NISQA-TTS naturalness score of 3.69 and the highest human misclassification rate of 61.9%.