Mapping Faroese in the Multilingual Representation Space: Insights for ASR Model Optimization (2025)

Abstract: ASR development for low-resource languages like Faroese faces significant challenges due to the scarcity of large, diverse datasets. While fine-tuning multilingual models using related languages is a common practice, there is no standardized method for selecting these auxiliary languages, leading to a computationally expensive trial-and-error process. By analyzing Faroese’s positioning among other languages in wav2vec2’s multilingual representation space, we find that Faroese’s closest neighbors are influenced not only by linguistic similarity but also by historical, phonetic, and cultural factors. These findings open new avenues for auxiliary language selection to improve Faroese ASR and underscore the potential value of data-driven factors in ASR fine-tuning.

## introduction low-resource languages, such as faroese, face unique challenges in asr development, primarily due to the lack of sufficiently large and varied datasets. recent advances in multilingual asr models have provided a promising avenue for cross-linguistic transfer, leveraging similarities between languages to enhance the performance of those with limited resources. it is common practice to fine-tune multilingual models for a target language by incorporating similar, closely related languages @xcite @xcite . however, currently there is no standardized procedure for selecting these languages. asr researchers often train multiple models with different language combinations to find the best set to enhance target language performance, a trial-and-error approach that is computationally costly as models grow larger. this underscores the need for more efficient methods. in this study, we focus on faroese, a lowresource insular scandinavian language. we explore its representation in meta's wav2vec2 xls-r 53 model (alexis conneau, 2020), and seek out its neighbors in this space, with the aim of extracting new insight for selection of auxiliary languages. our approach analyzes how languages are encoded within the model's multilingual representation space by measuring the distance between faroese and 102 languages from the google fleurs dataset (alexis conneau, 2022) at each model layer. since faroese is absent from google fleurs, we incorporated recordings from the ravnursson data set @xcite , currently the only asr-suitable faroese dataset, to better understand how the model perceives faroese in relation to other languages and to improve multilingual fine-tuning strategies. ## conclusion in conclusion, the representation spaces in wav2vec2 indicate that languages tend to clusters, as evidenced through nearest-neighbor analy-sis, clustering, and dimensionality reduction techniques. this analysis places faroese in proximity to gaelic languages, alongside germanic and nordic languages. the prominence of gaelic languages as close neighbors suggests that limiting comparisons to only the closest family members may overlook valuable insights, possibly related to historical phonetic and linguistic influences. such consideration will be further investigated in future work.