Semi-automatic voice comparison approach using spiking neural network for forensics - Siddanakatte Gopalaiah, Kruthika; Chandrakant Nagavi, Trisiladevi; Mahesha, Parashivamurthy (Institute of Advanced Engineering and Science, 2025)
This paper explores the application of a semi-automatic technique using spiking neural network (SNN) approach for forensic voice comparison (FVC), addressing the limitations of traditional methods that are time-consuming and subjective. By integrating machine learning with human expertise, the SNN, which mimics the brain’s processing of temporal information, is applied to analyze Australian English voice data in .flac format. The model leverages synaptic connection strengths modified by spike timing, allowing for flexible voice feature representation. Performance metrics, including confusion matrices and receiver operating characteristic (ROC) analysis, indicate the model’s accuracy of 94.21%, highlighting the effectiveness of the SNN-based approach for FVC.