TITLE:
Speaker Verification Based on Log-Likelihood Score Normalization
AUTHORS:
Wei Cao, Chunyan Liang, Shuxin Cao
KEYWORDS:
Speaker Verification, Score Normalization, Log-Likelihood Normalization, Zero Normalization, Test Normalization
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.11,
November
24,
2020
ABSTRACT: Due to differences in the distribution of scores for different trials, the performance of a speaker verification system will be seriously diminished if raw scores are directly used for detection with a unified threshold value. As such, the scores must be normalized. To tackle the shortcomings of score normalization methods, we propose a speaker verification system based on log-likelihood normalization (LLN). Without a priori knowledge, LLN increases the separation between scores of target and non-target speaker models, so as to improve score aliasing of “same-speaker” and “different-speaker” trials corresponding to the same test speech, enabling better discrimination and decision capability. The experiment shows that LLN is an effective method of scoring normalization.