Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F13%3A43919379" target="_blank" >RIV/49777513:23520/13:43919379 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-01931-4_13" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-01931-4_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-01931-4_13" target="_blank" >10.1007/978-3-319-01931-4_13</a>
Alternative languages
Result language
angličtina
Original language name
Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data
Original language 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 causeproblems. 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 dataset. 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 sourcedistribution 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 fo
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/TA01011264" target="_blank" >TA01011264: Elimination of the language barriers faced by the handicapped watchers of the Czech Television II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Speech and Computer
ISBN
978-3-319-01930-7
ISSN
0302-9743
e-ISSN
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Number of pages
8
Pages from-to
92-99
Publisher name
Springer
Place of publication
Cham
Event location
Pilzen, Czech Republic
Event date
Sep 1, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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