We compare the outcomes of multilingual and crosslingual training for related and unrelated Australian languages with similar phonologi- cal 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 lan- guage), and unseen data and language. Results indicate benefits of adapting the English base- line model for previously unseen languages.