Subspace Gaussian mixture models for speech recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F10%3APU91982" target="_blank" >RIV/00216305:26230/10:PU91982 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Subspace Gaussian mixture models for speech recognition
Original language description
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subspace. This style of acoustic model allows for a much more compact representation and gives better results than a conventional modeling approach, particularly with smaller amounts of training data.
Czech name
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Czech description
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Project
<a href="/en/project/FR-TI1%2F034" target="_blank" >FR-TI1/034: Multilingual recognition and search in speech for electronic dicionaries</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proc. International Conference on Acoustics, Speech, and Signal Processing
ISBN
978-1-4244-4296-6
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE Signal Processing Society
Place of publication
Dallas
Event location
Dallas
Event date
Mar 14, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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