Publication detail

Bayesian Models for Unit Discovery on a Very Low Resource Language

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/"
}

Documents

1802.06053.pdf