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KESIRAJU, S. PAPPAGARI, R. ONDEL YANG, L. BURGET, L. DEHAK, N. KHUDANPUR, S. ČERNOCKÝ, J. GANGASHETTY, S.
Originální název
Topic identification of spoken documents using unsupervised acoustic unit discovery
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální 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.
Klíčová slova
topic identification, acoustic unit discovery, unsupervised learning, non-parametric Bayesian models
Autoři
KESIRAJU, S.; PAPPAGARI, R.; ONDEL YANG, L.; BURGET, L.; DEHAK, N.; KHUDANPUR, S.; ČERNOCKÝ, J.; GANGASHETTY, S.
Vydáno
5. 3. 2017
Nakladatel
IEEE Signal Processing Society
Místo
New Orleans
ISBN
978-1-5090-4117-6
Kniha
Proceedings of ICASSP 2017
Strany od
5745
Strany do
5749
Strany počet
5
URL
https://www.fit.vut.cz/research/publication/11470/
BibTex
@inproceedings{BUT144450, author="Santosh {Kesiraju} and Raghavendra {Pappagari} and Lucas Antoine Francois {Ondel} and Lukáš {Burget} and Najim {Dehak} and Sanjeev {Khudanpur} and Jan {Černocký} and Suryakanth V {Gangashetty}", title="Topic identification of spoken documents using unsupervised acoustic unit discovery", booktitle="Proceedings of ICASSP 2017", year="2017", pages="5745--5749", publisher="IEEE Signal Processing Society", address="New Orleans", doi="10.1109/ICASSP.2017.7953257", isbn="978-1-5090-4117-6", url="https://www.fit.vut.cz/research/publication/11470/" }