# Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings

arXiv: https://arxiv.org/abs/2506.17690 · HTML: https://mlsanity.com/paper/2506.17690/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2025.

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

- [Unsupervised lexicon learning from speech is limited by representations rather than clustering](https://mlsanity.com/paper/2510.09225/) (2025)
- [Recovering the Zipfian Distribution in Unsupervised Term Discovery](https://mlsanity.com/paper/2606.10781/) (2026)

## Abstract (arXiv)

We introduce a new approach, the ContrastiveTransformer, that produces acoustic word embeddings (AWEs) for the purpose of very low-resource keyword spotting. The ContrastiveTransformer, an encoder-only model, directly optimises the embedding space using normalised temperature-scaled cross entropy (NT-Xent) loss. We use this model to perform keyword spotting for radio broadcasts in Luganda and Bambara, the latter a severely under-resourced language. We compare our model to various existing AWE approaches, including those constructed from large pre-trained self-supervised models, a recurrent encoder which previously used the NT-Xent loss, and a DTW baseline. We demonstrate that the proposed contrastive transformer approach offers performance improvements over all considered existing approaches to very low-resource keyword spotting in both languages.