  In recent years, neural models learned through self-supervised pretraining on
large scale multilingual text or speech data have exhibited promising results
for underresourced languages, especially when a relatively large amount of data
from related language(s) is available. While the technology has a potential for
facilitating tasks carried out in language documentation projects, such as
speech transcription, pretraining a multilingual model from scratch for every
new language would be highly impractical. We investigate the possibility for
adapting an existing multilingual wav2vec 2.0 model for a new language,
focusing on actual fieldwork data from a critically endangered tongue: Ainu.
Specifically, we (i) examine the feasibility of leveraging data from similar
languages also in fine-tuning; (ii) verify whether the model's performance can
be improved by further pretraining on target language data. Our results show
that continued pretraining is the most effective method to adapt a wav2vec 2.0
model for a new language and leads to considerable reduction in error rates.
Furthermore, we find that if a model pretrained on a related speech variety or
an unrelated language with similar phonological characteristics is available,
multilingual fine-tuning using additional data from that language can have
positive impact on speech recognition performance when there is very little
labeled data in the target language.
