A New Method of Score Normalization for Text-Independent Speaker Verification

When using a GMM-based text-independent speaker verification system,performance may be limited because of vari- ous output score distributions for different speaker-models and for different test segments.Taking into account these two differences,a new score normalization method,Entire normalization,is proposed.By scaling output scores,entire normalization aims to normalize all the score distributions to be similar.Experiments using entire normalization show significant improvements compared to standard normal- ization techniques.