@inproceedings{7a6ce0abe7154a309c5a4ec557c756ce,
title = "Maximum likelihood Linear Dimension Reduction of heteroscedastic feature for robust Speaker Recognition",
abstract = "This paper analyzes heteroscedasticity in i-vector for robust forensics and surveillance speaker recognition system. Linear Discriminant Analysis (LDA), a widely-used linear dimension reduction technique, assumes that classes are homoscedastic within a same covariance. In this paper it is assumed that general speech utterances contain both homoscedastic and heteroscedastic elements. We show the validity of this assumption by employing several analyses and also demonstrate that dimension reduction using principal components is feasible. To effectively handle the presence of heteroscedastic and homoscedastic elements, we propose a fusion approach of applying both LDA and Heteroscedastic-LDA (HLDA). The experiments are conducted to show its effectiveness and compare to other methods using the telephone database of National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) 2010 extended.",
keywords = "Algorithm design and analysis, Analytical models, Computational modeling, Speech, Speech processing, Switches, Transforms",
author = "Suwon Shon and Seongkyu Mun and Han, {David K.} and Hanseok Ko",
year = "2015",
month = oct,
day = "19",
doi = "10.1109/AVSS.2015.7301784",
language = "English",
series = "AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance",
note = "12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015 ; Conference date: 25-08-2015 Through 28-08-2015",
}