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@INPROCEEDINGS{Khelif_STO-MP-IST-160_2018,
         author = {Khelif, Khaled and yann Mombrun and Hazzani, Gideon and Motlicek, Petr and Madikeri, Srikanth and Sahito, Farhan and Kelly, Damien and Scarpatto, Luca and Chatzigavriil, Emmanouil and Backfried, Gerhard},
       projects = {Idiap, SIIP},
          month = may,
          title = {SIIP: An Innovative Speaker Identification Approach for Law Enforcement Agencies},
      booktitle = {Big Data and Artificial Intelligence for Military Decision Making},
           year = {2018},
          pages = {PT-1 - 1: PT-1 - 14},
      publisher = {STO},
   organization = {http://www.sto.nato.int/},
           note = {Meeting Proceedings RDP},
           isbn = {ISBN 978-92-837-2181-9},
            url = {https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-IST-160/MP-IST-160-PT-1.pdf},
            doi = {10.14339/STO-MP-IST-160},
       abstract = {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 proposed solution is based on development, integration and fusion of a series of individual speech analytic algorithms which includes speaker recognition, gender/age/language/accent identification, large vocabulary multilingual automatic speech-to-text transcription, expanded by 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. The implemented system presented in this paper has been introduced to the international community of law-enforcement agencies animated by Interpol. Preliminary feedbacks collected from end-users indicate their satisfaction with the proposed architecture and its functionality. They also expressed different exploitation needs that we will try to take into account in a further work.}
}