Bayesian Models for Unit Discovery on a Very Low Resource Language
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU130733" target="_blank" >RIV/00216305:26230/18:PU130733 - isvavai.cz</a>
Result on the web
<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11719" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11719</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP.2018.8461545" target="_blank" >10.1109/ICASSP.2018.8461545</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian Models for Unit Discovery on a Very Low Resource Language
Original language description
Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising results on artificial examples but still lack of in situ experiments. Our work applies state-of-the-art Bayesian models to unsupervised Acoustic Unit Discovery (AUD) in a real low-resource language scenario. We also show that Bayesian models can naturally integrate information from other resourceful languages by means of informative prior leading to more consistent discovered units. Finally, discovered acoustic units are used, either as the 1-best sequence or as a lattice, to perform word segmentation. Word segmentation results show that this Bayesian approach clearly outperforms a Segmental-DTW baseline on the same corpus.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
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 ICASSP 2018
ISBN
978-1-5386-4658-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
5939-5943
Publisher name
IEEE Signal Processing Society
Place of publication
Calgary
Event location
Calgary
Event date
Apr 15, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000446384606020