Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Vaněk, Jan | |
dc.contributor.author | Machlica, Lukáš | |
dc.contributor.author | Psutka, Josef V. | |
dc.contributor.author | Psutka, Josef | |
dc.date.accessioned | 2016-01-07T12:11:29Z | - |
dc.date.available | 2016-01-07T12:11:29Z | - |
dc.date.issued | 2013 | |
dc.identifier.citation | VANĚK, Jan; MACHLICA, Lukáš; PSUTKA, Josef V.; PSUTKA, Josef. Covariance matrix enhancement approach to train robust Gaussian mixture models of speech data. In: Speech and computer. Berlin: Springer, 2013, p. 92-99. (Lectures notes in computer science; 8113). ISBN 978-3-319-01930-7. | en |
dc.identifier.isbn | 978-3-319-01930-7 | |
dc.identifier.uri | http://www.kky.zcu.cz/cs/publications/JanVanek_2013_CovarianceMatrix | |
dc.identifier.uri | http://hdl.handle.net/11025/17161 | |
dc.format | 8 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | Springer | en |
dc.relation.ispartofseries | Lectures notes in computer; 8113 | en |
dc.rights | © Jan Vaněk - Lukáš Machlica - Josef V. Psutka - Josef Psutka | cs |
dc.subject | směsi Gaussovských modelů | cs |
dc.subject | plná kovariance | cs |
dc.subject | plná kovarianční matice | cs |
dc.subject | regularizace | cs |
dc.subject | automatické rozpoznávání řeči | cs |
dc.title | Covariance matrix enhancement approach to train robust Gaussian mixture models of speech data | en |
dc.type | článek | cs |
dc.type | article | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | 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 |
dc.subject.translated | Gaussian mixture models | en |
dc.subject.translated | full covariance | en |
dc.subject.translated | full covariance matrix | en |
dc.subject.translated | regularization | en |
dc.subject.translated | automatic speech recognition | en |
dc.identifier.doi | 10.1007/978-3-319-01931-4_13 | |
dc.type.status | Peer-reviewed | en |
Vyskytuje se v kolekcích: | Články / Articles (KIV) Články / Articles (KKY) |
Soubory připojené k záznamu:
Soubor | Popis | Velikost | Formát | |
---|---|---|---|---|
JanVanek_2013_CovarianceMatrix.pdf | Plný text | 253,94 kB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/17161
Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.