AuralGuard: Multi-View One-Class Learning for Generalizable and Calibrated Detection of AI-Generated Speech in the Wild

Official checkpoint repository and benchmark artifacts for the paper:
"AuralGuard: Multi-View One-Class Learning for Generalizable and Calibrated Detection of AI-Generated Speech in the Wild"
Target: Elsevier Computer Speech & Language / IEEE Transactions on Information Forensics and Security (IEEE TIFS)

• 📄 Camera-Ready Manuscript (17 Pages): paper/main.pdf
• 💻 Official Code Repository: github.com/MIHMahmudEli/auralguardv2
• 👥 Authors: Mohsin Ibna Hossain, Md. Muttakin, Sadman Hasan Miraj, and Mohammad Mahmudul Hasan (Corresponding Author)
• 🏛️ Affiliation: Department of Computer Science, American International University-Bangladesh (AIUB)

📊 Empirical Benchmark Results

Trained strictly once on ASVspoof 2019 LA and evaluated zero-shot out-of-domain across all benchmarks. Metrics report point estimates with 95% non-parametric bootstrap confidence intervals ($B=1,000$).

📈 Actual Manuscript Figures

1. Cross-Domain Generalization (Fig. 6)

2. Detection Error Tradeoff (DET) Curves (Fig. 7)

3. Non-Parametric 95% Bootstrap Confidence Intervals (Fig. 8)

4. Discrimination vs. Probability Calibration (Fig. 9)

5. Out-of-Domain Reliability Diagram on In-the-Wild (Fig. 10)

📦 Checkpoint Directory

🚀 Quick Usage

from huggingface_hub import hf_hub_download
import torch

# Download AuralGuard v2 checkpoint
ckpt_path = hf_hub_download(
    repo_id="MoshinAli/auralguard-checkpoints",
    filename="checkpoints/auralguard_v2/best.ckpt"
)
print(f"Downloaded checkpoint to: {ckpt_path}")