biostudies-literatureUnknown4Weber PThis paper reports on validations of an alpha version of the E³ Forensic Speech Science System (E³FS³) core software tools. This is an open-code human-supervised-automatic forensic-voice-comparison system based on x-vectors extracted using a type of Deep Neural Network (DNN) known as a Residual Network (ResNet). A benchmark validation was conducted using training and test data (<i>forensic_eval_01</i>) that have previously been used to assess the performance of multiple other forensic-voice-comparison systems. Performance equalled that of the best-performing system with previously published results for the <i>forensic_eval_01</i> test set. The system was then validated using two different populations (male speakers of Australian English and female speakers Forensic science international. Synergy100223https://www.ebi.ac.uk/biostudies/studies/S-EPMC8908042biostudies-literatureValidations of an alpha version of the E³ Forensic Speech Science System (E³FS³) core software tools.PMC8908042Gonzalez-Rodriguez JEnzinger EMorrison GSLozano-Diez AWeber PLabrador BRamos DfalseValidations of an alpha version of the E³ Forensic Speech Science System (E³FS³) core software tools.This paper reports on validations of an alpha version of the E³ Forensic Speech Science System (E³FS³) core software tools. This is an open-code human-supervised-automatic forensic-voice-comparison system based on x-vectors extracted using a type of Deep Neural Network (DNN) known as a Residual Network (ResNet). A benchmark validation was conducted using training and test data (<i>forensic_eval_01</i>) that have previously been used to assess the performance of multiple other forensic-voice-comparison systems. Performance equalled that of the best-performing system with previously published results for the <i>forensic_eval_01</i> test set. The system was then validated using two different populations (male speakers of Australian English and female speakers 2022-01-01T00:00:00Z20222025-04-19T13:45:57.967Z2024-12-04T03:40:10.852ZS-EPMC89080423528165710.1016/j.fsisyn.2022.100223