# Naturalness-Aware Curriculum Learning with Dynamic Temperature for Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2505.13976 · HTML: https://mlsanity.com/paper/2505.13976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2025.

## Built on by (most cited first, 2 of 2)

- [QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection](https://mlsanity.com/paper/2509.20679/) (2025)
- [ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection](https://mlsanity.com/paper/2609.03620/) (2026)

## Abstract (arXiv)

Recent advances in speech deepfake detection (SDD) have significantly improved artifacts-based detection in spoofed speech. However, most models overlook speech naturalness, a crucial cue for distinguishing bona fide speech from spoofed speech. This study proposes naturalness-aware curriculum learning, a novel training framework that leverages speech naturalness to enhance the robustness and generalization of SDD. This approach measures sample difficulty using both ground-truth labels and mean opinion scores, and adjusts the training schedule to progressively introduce more challenging samples. To further improve generalization, a dynamic temperature scaling method based on speech naturalness is incorporated into the training process. A 23% relative reduction in the EER was achieved in the experiments on the ASVspoof 2021 DF dataset, without modifying the model architecture. Ablation studies confirmed the effectiveness of naturalness-aware training strategies for SDD tasks.