The Likelihood Ratio (LR) is fundamental in presenting foren- sic speaker recognition (FSR) results. Despite its theoretical benefits, conventional LR estimation lacks transparency, imped- ing courtroom reliability assessment. In response, the Binary- Attribute-based Likelihood Ratio (BA-LR) framework models speech extracts based on the presence or absence of a set of speaker-specific attributes. It estimates the LR as a function of attribute-based LRs. Previous works demonstrated BA-LR’s three levels of interpretability: explicit computation of attribute- based LRs, explicit contribution of these LRs to the final LR and phonetic description of the attributes, promising a fully trans- parent FSR solution. This work adds an examination of LR cal- ibration using a forensically realistic database. Logistic regres- sion is used for calibration purposes, as well as for a regularized fusion of attribute-Log LRs. Results highlight robustness and generalization ability of BA-LR, particularly in forensics.