Full Covariance Gaussian Mixture Models Evaluation on GPU
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43916101" target="_blank" >RIV/49777513:23520/12:43916101 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Full Covariance Gaussian Mixture Models Evaluation on GPU
Original language description
Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e.g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC) instead of full covariances (FC) is used from the two reasons. The first reason is a~problem how to estimate robust FC matrices with a~limited training data set. The second reason is a~much higher computational cost during the GMM evaluation. The first reason was addressed in many recent publications. In contrast, this paper describes an efficient implementation on Graphic Processing Unit (GPU) of the FC-GMM evaluation, which addresses the second reason. The performance was tested on acoustic models for ASR, and it is shown that even a low-end laptop GPU is capable to evaluate a large acoustic model in a fraction of the real speech t
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Symposium on Signal Processing and Information Technology
ISBN
978-1-4673-5604-6
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
Institute of Electrical and Electronics Engineers ( IEEE )
Place of publication
345 E 47TH ST, NEW YORK, NY 10017
Event location
Vietnam, Ho Chi Minh City
Event date
Dec 12, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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