This paper presents an improved speaker recognition system for the summed channel evaluation tasks in the 2008 NIST SRE (SRE08) with multiple summed-channel excerpts for speaker training and one summed-channel excerpt for testing. The system includes three main modules in which a hybrid speaker purification and clustering algorithm is adopted to segregate the summed-channel speech, a common speaker identification is proposed by mapping multiple summed-channel excerpts for a common speaker cluster, and the GMM-SVM-NAP algorithm is used for the speaker recognition system. The system achieves an overall EER of 7.82% for all the trials and 4.19% for English trials in the SRE08 3summed-summed task.
Bibliographic reference. Sun, Hanwu / Ma, Bin (2014): "The NIST SRE summed channel speaker recognition system", In INTERSPEECH-2014, 1111-1114.