North Sámi Dialect Identification with Self-supervised Speech Models
Sofoklis Kakouros Katri Hiovain-Asikainen
The North S\'{a}mi (NS) language encapsulates four primary dialectal variants
that are related but that also have differences in their phonology, morphology,
and vocabulary. The unique geopolitical location of NS speakers means that in
many cases they are bilingual in S\'{a}mi as well as in the dominant state
language: Norwegian, Swedish, or Finnish. This enables us to study the NS
variants both with respect to the spoken state language and their acoustic
characteristics. In this paper, we investigate an extensive set of acoustic
features, including MFCCs and prosodic features, as well as state-of-the-art
self-supervised representations, namely, XLS-R, WavLM, and HuBERT, for the
automatic detection of the four NS variants. In addition, we examine how the
majority state language is reflected in the dialects. Our results show that NS
dialects are influenced by the state language and that the four dialects are
separable, reaching high classification accuracy, especially with the XLS-R
model.