Conference paper

CUMANI Sandro, PLCHOT Oldřich and KARAFIÁT Martin. Independent Component Analysis and MLLR Transforms for Speaker Identification. In: Proc. International Conference on Acoustics, Speech, and Signal P. Kyoto: IEEE Signal Processing Society, 2012, pp. 4365-4368. ISBN 978-1-4673-0044-5.
Publication language:english
Original title:Independent Component Analysis and MLLR Transforms for Speaker Identification
Title (cs):Analýza nezávislých komponent a MLLT transformace pro identifikaci řečníka
Pages:4365-4368
Proceedings:Proc. International Conference on Acoustics, Speech, and Signal P
Conference:The 37th International Conference on Acoustics, Speech, and Signal Processing
Place:Kyoto, JP
Year:2012
ISBN:978-1-4673-0044-5
Publisher:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2012/cumani_icassp2012_0004365.pdf [PDF]
Keywords
Speaker Recognition, MLLR, ICA, PLDA, SVM
Annotation
This paper describes the use of of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) techniques to reduce the dimensionality of high-level LVCSR features.
Abstract
In this paper, we explore the use of Independent Component Analysis (ICA) and Principal Component Analysis (PCA) techniques to reduce the dimensionality of high-level LVCSR features and at the same time to enable modelling them with state-of-the-art techniques like Probabilistic Linear Discriminant Analysis or Pairwise Support Vector Machines (PSVM). The high-level features are the coefficients from Constrained Maximum-Likelihood Linear Regression (CMLLR) and Maximum-Likelihood Linear Regression (MLLR) transforms estimated in an Automatic Speech Recognition (ASR) system. We also compare a classical approach of modeling every speaker by a single SVM classifier with the recent state-of-the-art modelling techniques in Speaker Identification. We report performance of the systems and score-level combination with a current state-of-the-art acoustic i-vector system on the NIST SRE2010 dataset.
BibTeX:
@INPROCEEDINGS{
   author = {Sandro Cumani and Old{\v{r}}ich Plchot and Martin
	Karafi{\'{a}}t},
   title = {Independent Component Analysis and MLLR Transforms for
	Speaker Identification},
   pages = {4365--4368},
   booktitle = {Proc. International Conference on Acoustics, Speech, and
	Signal P},
   year = {2012},
   location = {Kyoto, JP},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-4673-0044-5},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=9941}
}

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