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ONDEL YANG, L. GODARD, P. BESACIER, L. LARSEN, E. HASEGAWA-JOHNSON, M. SCHARENBORG, O. DUPOUX, E. BURGET, L. YVON, F. KHUDANPUR, S.
Original Title
Bayesian Models for Unit Discovery on a Very Low Resource Language
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Acoustic Unit Discovery, Low-Resource ASR, Bayesian Model, Informative Prior.
Authors
ONDEL YANG, L.; GODARD, P.; BESACIER, L.; LARSEN, E.; HASEGAWA-JOHNSON, M.; SCHARENBORG, O.; DUPOUX, E.; BURGET, L.; YVON, F.; KHUDANPUR, S.
Released
15. 4. 2018
Publisher
IEEE Signal Processing Society
Location
Calgary
ISBN
978-1-5386-4658-8
Book
Proceedings of ICASSP 2018
Pages from
5939
Pages to
5943
Pages count
5
URL
https://www.fit.vut.cz/research/publication/11719/
BibTex
@inproceedings{BUT155041, author="ONDEL YANG, L. and GODARD, P. and BESACIER, L. and LARSEN, E. and HASEGAWA-JOHNSON, M. and SCHARENBORG, O. and DUPOUX, E. and BURGET, L. and YVON, F. and KHUDANPUR, S.", title="Bayesian Models for Unit Discovery on a Very Low Resource Language", booktitle="Proceedings of ICASSP 2018", year="2018", pages="5939--5943", publisher="IEEE Signal Processing Society", address="Calgary", doi="10.1109/ICASSP.2018.8461545", isbn="978-1-5386-4658-8", url="https://www.fit.vut.cz/research/publication/11719/" }