Multilingual MFA: Forced Alignment on Low-Resource Related Languages
This work addresses speech processing for low-resource language communities, but it is incremental as it builds on existing forced alignment methods.
The study tackled the problem of forced alignment for low-resource Australian languages by comparing multilingual and crosslingual training approaches, finding that adapting a large English model improved performance on unseen languages.
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.