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Kenny, P., Boulianne, G. and Dumouchel, P. (2005) Eigenvoice Modeling with Sparse Training Data. IEEE Transactions on Speech and Audio Processing, 13, 345-354.
https://doi.org/10.1109/TSA.2004.840940

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.