# North S'ami Dialect Identification with Self-supervised Speech Models

arXiv: https://arxiv.org/abs/2305.11864 · HTML: https://mlsanity.com/paper/2305.11864/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 9 of 9)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition](https://mlsanity.com/paper/1803.04567/) (2018)
- [Automatic Dialect Detection in Arabic Broadcast Speech](https://mlsanity.com/paper/1509.06928/) (2015)
- [Dialect Identification of Spoken North S'ami Language Varieties Using Prosodic Features](https://mlsanity.com/paper/2003.10183/) (2020)

## Built on by (most cited first, 2 of 2)

- [Speech Analysis of Language Varieties in Italy](https://mlsanity.com/paper/2406.15862/) (2024)
- [Jointly Improving Dialect Identification and ASR in Indian Languages using Multimodal Feature Fusion](https://mlsanity.com/paper/2607.02862/) (2026)

## Abstract (arXiv)

The North S'ami (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'ami 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.