North S\'{a}mi Dialect Identification with Self-supervised Speech Models

TL;DR

This study explores the use of self-supervised speech models and acoustic features to accurately identify North Sámi dialects and analyze the influence of dominant state languages on these dialects.

Contribution

It introduces the application of XLS-R, WavLM, and HuBERT models for North Sámi dialect classification and examines the impact of state languages on dialect features.

Findings

High classification accuracy achieved with XLS-R model.

Dialectal differences are influenced by the dominant state language.

Self-supervised models effectively distinguish North Sámi dialects.

Abstract

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.