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Citation

Psutka Josef V. : Benefit of maximum likelihood linear transform (MLLT) used at different levels of covariance matrices clustering in ASR systems . Lecture Notes in Artificial Intelligence, 4629, p. 431-438, 2007.

Abstract

The paper discusses the benefit of a Maximum Likelihood Linear Transform (MLLT) applied on selected groups of covariance matrices. The matrices were chosen and clustered using phonetic knowledge. Results of experiments are compared with outcomes obtained for diagonal and full covariance matrices of a baseline system and also for widely used transforms based on Linear Discriminant Analysis (LDA), Heteroscedastic LDA (HLDA) and Smoothed HLDA (SHLDA).

Detail of publication

Title: Benefit of maximum likelihood linear transform (MLLT) used at different levels of covariance matrices clustering in ASR systems
Author: Psutka Josef V.
Language: English
Date of publication: 1 Jan 2007
Year: 2007
Type of publication: Papers in journals
Title of journal or book: Lecture Notes in Artificial Intelligence
Series: 4629
Page: 431 - 438
ISBN: 0302-9743
/ 2011-06-09 12:58:30 /

Keywords

Maximum likelihood linear transform, feature space decorrelation

BibTeX

@ARTICLE{PsutkaJosefV_2007_Benefitofmaximum,
 author = {Psutka Josef V.},
 title = {Benefit of maximum likelihood linear transform (MLLT) used at different levels of covariance matrices clustering in ASR systems},
 year = {2007},
 journal = {Lecture Notes in Artificial Intelligence},
 pages = {431-438},
 series = {4629},
 ISBN = {0302-9743},
 url = {http://www.kky.zcu.cz/en/publications/PsutkaJosefV_2007_Benefitofmaximum},
}