Bayesian Models for Unit Discovery on a Very Low Resource Language

Lucas Ondel; Pierre Godard; Laurent Besacier; Elin Larsen; Mark Hasegawa-Johnson; Odette Scharenborg; Emmanuel Dupoux; Lukás Burget

ICASSP 2018accepted0 citations

Bayesian Models for Unit Discovery on a Very Low Resource Language

Lucas Ondel, Pierre Godard, Laurent Besacier, Elin Larsen, Mark Hasegawa-Johnson, Odette Scharenborg, Emmanuel Dupoux, Lukás Burget

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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 I-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.

BibTeX

@inproceedings{icassp2018_bayesianmodelsfo,
  title = {Bayesian Models for Unit Discovery on a Very Low Resource Language},
  author = {Lucas Ondel and Pierre Godard and Laurent Besacier and Elin Larsen and Mark Hasegawa-Johnson and Odette Scharenborg and Emmanuel Dupoux and Lukás Burget and François Yvon and Sanjeev Khudanpur},
  booktitle = {ICASSP 2018},
  year = {2018}
}