# Cross-lingual topic prediction for speech using translations

arXiv: https://arxiv.org/abs/1908.11425 · HTML: https://mlsanity.com/paper/1908.11425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Strategies for improving low resource speech to text translation relying on pre-trained ASR models](https://mlsanity.com/paper/2306.00208/) (2023)

## Abstract (arXiv)

Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount of speech in the low-resource language is paired with text translations in a high-resource language. We develop an effective cross-lingual topic classifier by training on just 20 hours of translated speech, using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify the topic of 1-minute speech segments over 70% of the time - a 20% improvement over a majority-class baseline. Such a system could be useful for humanitarian applications like crisis response, where incoming speech in a foreign low-resource language must be quickly assessed for further action.