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Laskar, M.A. and Laskar, R.H. (2020) A Fuzzy-Clustering-Based Hierarchical I-Vector/Probabilistic Linear Discriminant Analysis System for Text-Dependent Speaker Verification. Expert Systems, 37, e12524.
https://doi.org/10.1111/exsy.12496

has been cited by the following article:

• 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.