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This paper presents a fine-tuning approach for automatic speech recognition (ASR) that exploits linguistic similarities between dialects to address data scarcity and imbalance in dialectally rich, low-resource languages. The approach is evaluated on Occitan, a low-resource language composed of six main dialects with unevenly distributed resources. For the better-resourced dialects, Languedocien and Gascon, a two-step fine-tuning is applied to improve recognition performance by exploiting inter-dialectal information. For the less-represented dialects, we assess the effectiveness of knowledge transfer from the better-resourced variants, analyzing the model’s ability to generalize to sparsely represented dialects. The results show that leveraging linguistic proximity between dialects is an effective strategy to enhance ASR performance in low-resource settings with high dialectal variability.
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We introduce ForumOccitania, a new Occitan corpus of posts from an online forum, covering a range of topics and dialects. While some existing datasets for this low-resource language include labels of varieties within the dialect continuum, we go one step further by providing metadata pertaining to sociolinguistic factors of language variation (dialect, geographical location, age, proficiency), extracted from self-declared user profiles. We carry out statistical and qualitative analyses, as well as preliminary experiments on unsupervised dialect identification. Our results show that (i) most of the contents is written in Occitan, with the classical spelling conventions, and by young new speakers, (ii) posts display a strong presence of dialectal features from four major Occitan varieties (Lemosin, Lengadocian, Gascon, Provençau), and (iii) a simple topic modelling approach introduced by Kuparinen and Scherrer (2024) effectively detects salient features of these four varieties, but also reveals finer-grained diatopical variation tendencies.
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Occitan is a Romance language spoken mostly in the South of France and characterised by rich dialectal variation, which can pose problems for certain NLP tools. This shortfall is largely attributable to the scarcity of dialect-annotated corpora, in a context where linguistic classification within the Occitan dialect continuum is still debated and major nomenclatures, such as ISO 639, fail to provide granular codes for varieties below the generic “Occitan” label. In this paper, we introduce OcWikiDialects, a new dataset comprising articles from the Occitan Wikipedia. The corpus features rich metadata, including dialect labels, and is segmented at both paragraph and sentence levels. Combined with previously released datasets, we explore approaches for Occitan dialect identification by training three types of model on up to 8 labels: linear SVM classifiers based on word and character n-grams, FastText classifiers based on pretrained vectors, and BERT-based neural classifiers adapted through fine-tuning. Evaluations across in- and out-of-domain test sets demonstrate the substantial impact of our new dataset for the task. However, a peak macro-averaged F1 score of 58.15 underscores persistent challenges for underrepresented Occitan varieties, supported by our per-dialect analysis. Code, dataset and models are available: https://github.com/DEFI-COLaF/OcWikiDialects.
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Description du processus de développement et d’anonymisation d’un corpus en occitan issu d’un forum en ligne et riche en métadonnées liées à la variation dialectale.
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Automatic Speech Recognition (ASR) has advanced considerably in recent years, largely fueled by breakthroughs in self-supervised learning and the increasing availability of large-scale multilingual speech corpora. Models such as Whisper have demonstrated remarkable performance across numerous languages. However, despite its strong multilingual coverage, performance remains highly variable across languages. In this study, we fine-tune Whisper in four regional languages spoken in France, each selected to represent distinct linguistic and resource conditions: Basque, Occitan, Alsatian, and Shimaore. Performance is increased by fine-tuning for each language, but to significantly varying degrees. This is due to a combination of varying data quantity and quality, language similarity to an already well-covered language, and transcription standardisation: all things that low-resource languages may lack.
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Cet article dresse un état de l'art de la traduction automatique et de son évaluation pour les langues à variation dialectale, et en particulier pour les continuums dialectaux. Pour illustrer cet état de l'art, nous proposons une série d'expériences préliminaires sur le continuum occitan, afin de dresser un état des performances des systèmes existants pour la traduction depuis et vers plusieurs variétés d'occitan. Nos résultats indiquent d'une part des performances globalement satisfaisantes pour la traduction vers le français et l'anglais. D'autre part, des analyses mélangées à des outils d'identification de langues sur les prédictions vers l'occitan mettent en lumière la capacité de la plupart des systèmes évalués à générer des textes dans cette langue (y compris en zero-shot ), mais révèlent aussi des limitations en termes d'évaluation de la diversité dialectale dans les traductions proposées.