{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["4"],"submitter":["Weber P"],"pubmed_abstract":["This paper reports on validations of an alpha version of the E<sup>3</sup> Forensic Speech Science System (E<sup>3</sup>FS<sup>3</sup>) 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 "],"journal":["Forensic science international. Synergy"],"pagination":["100223"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8908042"],"repository":["biostudies-literature"],"pubmed_title":["Validations of an alpha version of the E<sup>3</sup> Forensic Speech Science System (E<sup>3</sup>FS<sup>3</sup>) core software tools."],"pmcid":["PMC8908042"],"pubmed_authors":["Gonzalez-Rodriguez J","Enzinger E","Morrison GS","Lozano-Diez A","Weber P","Labrador B","Ramos D"],"additional_accession":[]},"is_claimable":false,"name":"Validations of an alpha version of the E<sup>3</sup> Forensic Speech Science System (E<sup>3</sup>FS<sup>3</sup>) core software tools.","description":"This paper reports on validations of an alpha version of the E<sup>3</sup> Forensic Speech Science System (E<sup>3</sup>FS<sup>3</sup>) 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 ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-19T13:45:57.967Z","creation":"2024-12-04T03:40:10.852Z"},"accession":"S-EPMC8908042","cross_references":{"pubmed":["35281657"],"doi":["10.1016/j.fsisyn.2022.100223"]}}