# AI-Synthesized Voice Detection Using Neural Vocoder Artifacts

arXiv: https://arxiv.org/abs/2304.13085 · HTML: https://mlsanity.com/paper/2304.13085/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 30 of 41)

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- [VoiceWukong: Benchmarking Deepfake Voice Detection](https://mlsanity.com/paper/2409.06348/) (2024)
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- [Multi-level SSL Feature Gating for Audio Deepfake Detection](https://mlsanity.com/paper/2509.03409/) (2025)
- [A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations](https://mlsanity.com/paper/2506.03425/) (2025)
- [AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors](https://mlsanity.com/paper/2509.21597/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://mlsanity.com/paper/2601.02944/) (2026)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds](https://mlsanity.com/paper/2509.04345/) (2025)

## Abstract (arXiv)

Advancements in AI-synthesized human voices have created a growing threat of impersonation and disinformation, making it crucial to develop methods to detect synthetic human voices. This study proposes a new approach to identifying synthetic human voices by detecting artifacts of vocoders in audio signals. Most DeepFake audio synthesis models use a neural vocoder, a neural network that generates waveforms from temporal-frequency representations like mel-spectrograms. By identifying neural vocoder processing in audio, we can determine if a sample is synthesized. To detect synthetic human voices, we introduce a multi-task learning framework for a binary-class RawNet2 model that shares the feature extractor with a vocoder identification module. By treating vocoder identification as a pretext task, we constrain the feature extractor to focus on vocoder artifacts and provide discriminative features for the final binary classifier. Our experiments show that the improved RawNet2 model based on vocoder identification achieves high classification performance on the binary task overall.