EFFECTIVE BACKGROUND DATA SELECTION IN SVM SPEAKER RECOGNITION FOR UNSEEN TEST ENVIRONMENT: MORE IS NOT ALWAYS BETTER

摘要

This study focuses on determining a procedure to select effective negative examples for development of improved Support Vector Machine (SVM) based speaker recognition. Selection of a background dataset, comprising of a group of negative examples, is critical in development of an effective decision surface between the primary speaker and outside speaker rejection space. Previous studies generally fix the number of examples based on development data for system performance evaluation, while for real applications this does not guarantee sustained performance for unseen data. In the proposed method, the error is estimated on the support vector to select the background dataset, thereby by customizing the background dataset for each enrollment speaker instead of training models with a fixed background data. The proposed method finds the equivalent or improved EER and DCF compared with the previous SVM-based studies, and provides consistent performance for unseen data. The method improves the 6percent relative improvement on EER and DCF for NIST SRE 2010.