North Sámi Dialect Identification with Self-supervised Speech Models
This work addresses dialect identification for the North Sámi language, which is important for linguistic preservation and speech technology applications, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically identifying four North Sámi dialects using acoustic features and self-supervised speech models, achieving high classification accuracy, particularly with the XLS-R model.
The North Sámi (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 Sámi 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.