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KESIRAJU, S. PAPPAGARI, R. ONDEL YANG, L. BURGET, L. DEHAK, N. KHUDANPUR, S. ČERNOCKÝ, J. GANGASHETTY, S.
Original Title
Topic identification of spoken documents using unsupervised acoustic unit discovery
Type
conference paper
Language
English
Original Abstract
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.
Keywords
topic identification, acoustic unit discovery, unsupervised learning, non-parametric Bayesian models
Authors
KESIRAJU, S.; PAPPAGARI, R.; ONDEL YANG, L.; BURGET, L.; DEHAK, N.; KHUDANPUR, S.; ČERNOCKÝ, J.; GANGASHETTY, S.
Released
5. 3. 2017
Publisher
IEEE Signal Processing Society
Location
New Orleans
ISBN
978-1-5090-4117-6
Book
Proceedings of ICASSP 2017
Pages from
5745
Pages to
5749
Pages count
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/" }