Topic identification of spoken documents using unsupervised acoustic unit discovery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126427" target="_blank" >RIV/00216305:26230/17:PU126427 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/11470/" target="_blank" >https://www.fit.vut.cz/research/publication/11470/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP.2017.7953257" target="_blank" >10.1109/ICASSP.2017.7953257</a>
Alternative languages
Result language
angličtina
Original language name
Topic identification of spoken documents using unsupervised acoustic unit discovery
Original language description
This paper investigates the application of unsupervised acoustic unit discovery for topic identification (topic ID) of spoken audio documents. The acoustic unit discovery method is based on a nonparametric Bayesian phone-loop model that segments a speech utterance into phone-like categories. The discovered phone-like (acoustic) units are further fed into the conventional topic ID framework. Using multilingual bottleneck features for the acoustic unit discovery, we show that the proposed method outperforms other systems that are based on cross-lingual phoneme recognizer.
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)
Others
Publication year
2017
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 2017
ISBN
978-1-5090-4117-6
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
5745-5749
Publisher name
IEEE Signal Processing Society
Place of publication
New Orleans
Event location
New Orleans, USA
Event date
Mar 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000414286205181