@inproceedings{3905d4a91aa64503820efa0dc260f636,
title = "Optimization of Gaussian mixture model parameters for speaker identification",
abstract = "Gaussian mixture model (GMM) [1] has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on the genetic algorithm (GA). It utilizes the global searching capability of the GA and combines the effectiveness of the ML method.",
keywords = "Genetic Algorithm, Gaussian Mixture Model, Speaker Identification, Female Speaker, Speaker Model",
author = "HONG, {Q. Y.} and Sam KWONG and WANG, {H. L.}",
year = "2004",
doi = "10.1007/978-3-540-24855-2_141",
language = "English",
isbn = "9783540223436",
series = "Lecture Notes in Computer Science ",
publisher = "Springer Berlin",
pages = "1310--1311",
editor = "Riccardo POLI and Owen HOLLAND and Wolfgang BANZHAF and Hans-Georg BEYER and Edmund BURKE and Paul DARWEN and Dipankar DASGUPTA and Dario FLOREANO and James FOSTER and Mark HARMAN and LANZI, {Pier Luca} and Lee SPECTOR and TETTAMANZI, {Andrea G. B.} and Dirk THIERENS and TYRRELL, {Andrew M.}",
booktitle = "Genetic and Evolutionary Computation : GECCO 2004 : Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26–30, 2004 Proceedings, Part II",
address = "Germany",
note = "2004 Genetic and Evolutionary Computation Conference, GECCO 2004 ; Conference date: 26-06-2004 Through 30-06-2004",
}