CONF Khelif_EISIC2017_2017/IDIAP Towards a breakthrough Speaker Identification approach for Law Enforcement Agencies: SIIP Khelif, Khaled yann Mombrun, Backfried, Gerhard Sahito, Farhan Scarpatto, Luca Motlicek, Petr Kelly, Damien Hazzani, Gideon Chatzigavriil, Emmanouil Madikeri, Srikanth audio and voice analysis Forensics LEA OSINT Speaker identification EXTERNAL https://publications.idiap.ch/attachments/papers/2017/Khelif_EISIC2017_2017.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Khelif_Idiap-RR-29-2017 Related documents European Intelligence and Security Informatics Conference (EISIC) 2017 Athenes, Greece 2017 IEEE Computer Society 32-39 978-1-5386-2385-5/17 http://www.eisic.eu/eisic2017/organization.aspx URL 10.1109/EISIC.2017.14 doi This paper describes SIIP (Speaker Identification Integrated Project) a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms which includes speaker model recognition, gender identification, age identification, language and accent identification, keyword and taxonomy spotting. A full integrated system is proposed ensuring multisource data management, advanced voice analysis, information sharing and efficient and consistent man-machine interactions. REPORT Khelif_Idiap-RR-29-2017/IDIAP Towards a breakthrough speaker identification approach for law enforcement agencies Khelif, Khaled yann Mombrun, Motlicek, Petr Backfried, Gerhard Kelly, Damien Sahito, Farhan Hazzani, Gideon Scarpatto, Luca Chatzigavriil, Emmanouil Madikeri, Srikanth audio and voice analysis Forensics LEA OSINT Speaker identification EXTERNAL https://publications.idiap.ch/attachments/reports/2017/Khelif_Idiap-RR-29-2017.pdf PUBLIC Idiap-RR-29-2017 2017 Idiap Rue Marconi 19, Martigny, Switzerland October 2017 This paper describes a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms which includes speaker model recognition, gender identification, age identification, language and accent identification, keyword and taxonomy spotting. A full integrated system is proposed ensuring multisource data management, advanced voice analysis, information sharing and efficient and consistent man-machine interactions.