Abstract: Speech is now routine evidence in criminal investigations, but forensic audio rarely matches the clean assumptions of standard speaker recognition. Clips are short, noisy, codec-compressed, and channel-mismatched, and they are increasingly exposed to replay and synthetic speech manipulation. Therefore, the cast criminal voice identification is forensic audio data mining, aiming to extract a stable identity structure from heterogeneous and potentially adversarial evidence, while respecting operational and privacy constraints. In this study, a novel ForenVoice-Secure system is proposed, a unified pipeline that combines robust representation learning, spoof-aware decisioning, and privacy-preserving training. Audio is mapped to log-Mel spectrograms and encoded with a CNN, while an LSTM aggregates temporal identity cues from irregular utterances. Robustness is improved through multi-task learning (identity + spoof), adversarial training, and spectro-temporal consistency checks for replay/deepfake artifacts. Privacy is addressed using federated learning, keeping raw recordings local and sharing only model updates. Experiments on VoxCeleb2, ASVspoof 2021, and a forensic-style speaker comparison corpus achieve statistically significant performance gains, 98.43% mean identification accuracy with strong class-balanced performance (macro F1 = 98.10%, precision = 98.22%, recall = 98.01%) and statistically significant gains over strong baselines across repeated folds (F1: p=8.0×〖10〗^(-4); precision: p=1.1×〖10〗^(-3); recall: p=9.0×〖10〗^(-4)). The model remains lightweight (≈4.3M parameters, ≈1.2 GFLOPs per 3 s), enabling near real-time inference with modest overhead from consistency checks (<6%). Overall, ForenVoice-Secure provides a compact and reproducible forensic audio data mining framework for scalable, spoof-resilient, privacy-aware law-enforcement identification.

Mubarak Albathan. “ForenVoice-Secure: Robust and Privacy-Aware Audio Data Mining for Forensic Speaker Identification”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170115

@article{Albathan2026,
title = {ForenVoice-Secure: Robust and Privacy-Aware Audio Data Mining for Forensic Speaker Identification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170115},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170115},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Mubarak Albathan}
}