Multi-task Neural Networks For Speech Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU111991" target="_blank" >RIV/00216305:26230/14:PU111991 - isvavai.cz</a>
Result on the web
<a href="http://www.feec.vutbr.cz/EEICT/wp-content/uploads/2014/04/2014-sbornik-mgr.pdf" target="_blank" >http://www.feec.vutbr.cz/EEICT/wp-content/uploads/2014/04/2014-sbornik-mgr.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Multi-task Neural Networks For Speech Recognition
Original language description
The article covers experiments on TIMIT database exploring the possibility of using multitask neural networks for speech recognition. Multi-task neural networks are deep neural networks solving several different classification tasks simultaneously. The secondary tasks chosen for the experiments are gender, context, articulatory characteristics and a fusion of some of them. The experiments show that addition of such tasks can enhance the learning and improve recognition accuracy.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 20th Student Conference, EEICT 2014
ISBN
978-80-214-4923-7
ISSN
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e-ISSN
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Number of pages
3
Pages from-to
24-26
Publisher name
Brno University of Technology
Place of publication
Brno
Event location
Brno
Event date
Apr 24, 2014
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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