# Bayesian Models for Unit Discovery on a Very Low Resource Language

arXiv: https://arxiv.org/abs/1802.06053 · HTML: https://mlsanity.com/paper/1802.06053/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation](https://mlsanity.com/paper/2104.00994/) (2021)
- [A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery](https://mlsanity.com/paper/2206.11706/) (2022)

## Abstract (arXiv)

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.