Forensic Automatic Speaker Recognition Based on Likelihood Ratio Using Acoustic-phonetic Features Measured Automatically

Forensic speaker recognition is experiencing a remarkable paradigm shift in terms of the evaluation framework and presentation of voice evidence.This paper proposes a new method of forensic automatic speaker recognition using the likelihood ratio framework to quantify the strength of voice evidence.The proposed method uses a reference database to calculate the within-and between-speaker variability.Some acoustic-phonetic features are extracted automatically using the software VbiceSauce.The effectiveness of the approach was tested using two Mandarin databases:A mobile telephone database and a landline database.The experimenfs results indicate that these acoustic-phonetic features do have some discriminating potential and are worth trying in discrimination.The automatic acoustic-phonetic features have acceptable discriminative performance and can provide more reliable results in evidence analysis when fused with other kind of voice features.