About: Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007%2F978-3-319-01931-4_13
Description	An estimation of parameters of a multivariate Gaussian Mixture Model is usually based on a criterion (e.g. Maximum Likelihood) that is focused mostly on training data. Therefore, testing data, which were not seen during the training procedure, may cause problems. Moreover, numerical instabilities can occur (e.g. for low-occupied Gaussians especially when working with full-covariance matrices in high-dimensional spaces). Another question concerns the number of Gaussians to be trained for a specific data set. The approach proposed in this paper can handle all these issues. It is based on an assumption that the training and testing data were generated from the same source distribution. The key part of the approach is to use a criterion based on the source distribution rather than using the training data itself. It is shown how to modify an estimation procedure in order to fit the source distribution better (despite the fact that it is unknown), and subsequently new estimation algorithm for diagonal- as well as full-covariance matrices is derived and tested. An estimation of parameters of a multivariate Gaussian Mixture Model is usually based on a criterion (e.g. Maximum Likelihood) that is focused mostly on training data. Therefore, testing data, which were not seen during the training procedure, may cause problems. Moreover, numerical instabilities can occur (e.g. for low-occupied Gaussians especially when working with full-covariance matrices in high-dimensional spaces). Another question concerns the number of Gaussians to be trained for a specific data set. The approach proposed in this paper can handle all these issues. It is based on an assumption that the training and testing data were generated from the same source distribution. The key part of the approach is to use a criterion based on the source distribution rather than using the training data itself. It is shown how to modify an estimation procedure in order to fit the source distribution better (despite the fact that it is unknown), and subsequently new estimation algorithm for diagonal- as well as full-covariance matrices is derived and tested. (en)
Title	Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data (en)
skos:prefLabel	Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data (en)
skos:notation	RIV/49777513:23520/13:43919379!RIV14-TA0-23520___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(TA01011264)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Vaněk, Jan Psutka, Josef Psutka jr., Josef Machlica, Lukáš
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	67224
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/13:43919379
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Automatic Speech Recognition; Regularization; Full Covariance Matrix; Full Covariance; Gaussian Mixture Models (en)
http://linked.open.../riv/klicoveSlovo	Full Covariance Matrix Regularization Gaussian Mixture Models Automatic Speech Recognition Full Covariance
http://linked.open...ontrolniKodProRIV	[0989B2E77021]
http://linked.open...v/mistoKonaniAkce	Pilzen, Czech Republic
http://linked.open...i/riv/mistoVydani	Cham
http://linked.open...i/riv/nazevZdroje	Speech and Computer
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	4 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	Elimination of the language barriers faced by the handicapped watchers of the Czech Television II
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Psutka jr., Josef Psutka, Josef Vaněk, Jan Machlica, Lukáš
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-09-01 (xsd:date)
issn	0302-9743
number of pages	8 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-01931-4_13
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-319-01930-7
http://localhost/t...ganizacniJednotka	23520

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software