Článek ve sborníku konference

KESIRAJU Santosh, PAPPAGARI Raghavendra, ONDEL Lucas, BURGET Lukáš, DEHAK Najim, KHUDANPUR Sanjeev, ČERNOCKÝ Jan a GANGASHETTY Suryakanth V. Topic identification of spoken documents using unsupervised acoustic unit discovery. In: Proceedings of ICASSP 2017. New Orleans: IEEE Signal Processing Society, 2017, s. 5745-5749. ISBN 978-1-5090-4117-6.
Jazyk publikace:angličtina
Název publikace:Topic identification of spoken documents using unsupervised acoustic unit discovery
Název (cs):Identifikace témat z mluvených dokumentů pomocí automatického hledání řečových jednotek
Strany:5745-5749
Sborník:Proceedings of ICASSP 2017
Konference:42nd IEEE International Conference on Acoustics, Speech and Signal Processing
Místo vydání:New Orleans, US
Rok:2017
ISBN:978-1-5090-4117-6
DOI:10.1109/ICASSP.2017.7953257
Vydavatel:IEEE Signal Processing Society
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2017/kesiraju_icassp2017_0005745.pdf [PDF]
Klíčová slova
topic identification, acoustic unit discovery, unsupervised learning, non-parametric Bayesian models
Anotace
Článek pojednává o identifikaci témat z mluvených dokumentů pomocí automatického hledání řečových jednotek.
Abstrakt
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.
BibTeX:
@INPROCEEDINGS{
   author = {Santosh Kesiraju and Raghavendra Pappagari and
	Lucas Ondel and Luk{\'{a}}{\v{s}} Burget and Najim
	Dehak and Sanjeev Khudanpur and Jan
	{\v{C}}ernock{\'{y}} and V Suryakanth Gangashetty},
   title = {Topic identification of spoken documents using
	unsupervised acoustic unit discovery},
   pages = {5745--5749},
   booktitle = {Proceedings of ICASSP 2017},
   year = 2017,
   location = {New Orleans, US},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-5090-4117-6},
   doi = {10.1109/ICASSP.2017.7953257},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.cs?id=11470}
}

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