Development of a Speaker Recognition Solution in Vidispine

Abstract: A video database contains an enormous amount of information. In order to search through the database, metadata can be attached to each video. One such type of metadata can be labels containing speakers and where they are speaking. With the help of speaker recognition this type of metadata can automatically be assigned to each video. In this thesis a speaker recognition plug-in for Vidispine, an API media asset management platform, is presented. The plug-in was developed with the help of the LIUM SpkDiarization toolkit for speaker diarization and the ALIZE/LIA RAL toolkit for speaker identification. The choice of using the method of GMM-UBM that ALIZE/LIA RAL offers, was made through an in-depth theoretical study of different identification methods. The in-depth study is presented in its own chapter. The goal of the plug-in was to perform an identification rate of 85%. However, the results unfortunately became as low as 63%. Among the issues the plug-in faces, its low performance on female speaker was shown to be crucial.