ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

i-Vector Selection for Effective PLDA Modeling in Speaker Recognition

Johan Rohdin, Sangeeta Biswas, Koichi Shinoda

Data selection is an important issue in speaker recognition. In the previous studies, the data selection for background models such as UBM, or SVM background, have been addressed. In this paper, we address the data selection for a PLDA model which is the state-of-art for i-vector scoring. We propose a modified version of k-NN where k is optimized by using local distance-based outlier factor (LDOF). We name this as flexible k-NN or fk-NN. Contrary to the previous studies, our approach does not make use of any other meta-information than gender information such as speech duration, or transmission channel, for selecting data for PLDA models. Our fk-NN obtained significant performance improvements on both male and female trials of NIST Speaker Recognition Evaluation (SRE) 2006 core task, NIST SRE 2008 core task (condition-6) and NIST SRE 2010 coreext-coreext task (condition-5).