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.
View on arXiv@article{tosolini2025_2504.07315,
title={ Multilingual MFA: Forced Alignment on Low-Resource Related Languages },
author={ Alessio Tosolini and Claire Bowern },
journal={arXiv preprint arXiv:2504.07315},
year={ 2025 }
}