Forensic Speaker

Forensic speaker recognition aims to reliably identify individuals based on their voice recordings, a crucial task in legal investigations. Current research heavily focuses on improving the accuracy and robustness of automatic speaker recognition systems, particularly using deep learning architectures like ECAPA-TDNN and x-vectors, addressing challenges such as language mismatch, diverse acoustic environments (including noisy field recordings), and limited data availability for certain languages. These advancements enhance the reliability and objectivity of voice identification in forensic contexts, impacting both legal proceedings and the broader field of audio forensics.