# Multilingual acoustic word embeddings for zero-resource languages

arXiv: https://arxiv.org/abs/2401.10543 · HTML: https://mlsanity.com/paper/2401.10543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2024.

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

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Zero-Shot KWS for Children's Speech using Layer-Wise Features from SSL Models](https://mlsanity.com/paper/2508.21248/) (2025)

## Abstract (arXiv)

This research addresses the challenge of developing speech applications for zero-resource languages that lack labelled data. It specifically uses acoustic word embedding (AWE) -- fixed-dimensional representations of variable-duration speech segments -- employing multilingual transfer, where labelled data from several well-resourced languages are used for pertaining. The study introduces a new neural network that outperforms existing AWE models on zero-resource languages. It explores the impact of the choice of well-resourced languages. AWEs are applied to a keyword-spotting system for hate speech detection in Swahili radio broadcasts, demonstrating robustness in real-world scenarios. Additionally, novel semantic AWE models improve semantic query-by-example search.