Collection and analysis of multi-condition audio recordings for forensic automatic speaker recognition




Forensic automatic speaker recognition, Real case recordings, Validation, Match and mismatch condition, Calibration


The major aim of the project presented here is to compile a corpus from real case recordings to validate more recording conditions and languages under match and mismatch conditions for forensic automatic speaker recognition (FASR). The challenges and limitations of compiling a real case corpus are explained. First results of validation tests are presented for male speakers of German in the match condition [voice message – voice message] as well as in the mismatch condition [voice message – telephone]. Results for the match condition [voice message] are compared to previous findings for the match condition [telephone]. Variations of performance metrics such as Equal Error Rate (EER) and log-likelihoodratio cost (Cllr) are discussed with respect to effects of normalisation and calibration, and patterns of score distributions are analysed using Tippett plots.