# Finnish Dialect Identification: The Effect of Audio and Text

arXiv: https://arxiv.org/abs/2111.03800 · HTML: https://mlsanity.com/paper/2111.03800/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Multilingual is not enough: BERT for Finnish](https://mlsanity.com/paper/1912.07076/) (2019)
- [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://mlsanity.com/paper/1903.11269/) (2019)
- [Multi-Dialect Arabic BERT for Country-Level Dialect Identification](https://mlsanity.com/paper/2007.05612/) (2020)
- [Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity](https://mlsanity.com/paper/2009.02685/) (2020)
- [Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography](https://mlsanity.com/paper/2107.03266/) (2021)
- [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, 8 of 8)

- [DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages](https://mlsanity.com/paper/2403.11009/) (2024)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks](https://mlsanity.com/paper/2203.12906/) (2022)
- [RoDia: A New Dataset for Romanian Dialect Identification from Speech](https://mlsanity.com/paper/2309.03378/) (2023)
- [The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties](https://mlsanity.com/paper/2509.07139/) (2025)
- [Speech Analysis of Language Varieties in Italy](https://mlsanity.com/paper/2406.15862/) (2024)
- [Processing M.A. Castr'en's Materials: Multilingual Typed and Handwritten Manuscripts](https://mlsanity.com/paper/2112.14153/) (2021)
- [Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance](https://mlsanity.com/paper/2308.04886/) (2023)

## Abstract (arXiv)

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.