# Multilingual MFA: Forced Alignment on Low-Resource Related Languages

arXiv: https://arxiv.org/abs/2504.07315 · HTML: https://mlsanity.com/paper/2504.07315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens](https://mlsanity.com/paper/2402.02302/) (2024)
- [Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages](https://mlsanity.com/paper/2103.14583/) (2021)

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

- [Montreal Forced Aligner and the state of speech-to-text alignment in 2026](https://mlsanity.com/paper/2606.18466/) (2026)

## Abstract (arXiv)

We compare the outcomes of multilingual and crosslingual training for related and unrelated Australian languages with similar phonological inventories. We use the Montreal Forced Aligner to train acoustic models from scratch and adapt a large English model, evaluating results against seen data, unseen data (seen language), and unseen data and language. Results indicate benefits of adapting the English baseline model for previously unseen languages.