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.

This study challenges the current paradigm shift in machine translation, where large language models (LLMs) are gaining prominence over traditional neural machine translation models, with a focus on English-to-Faroese translation. We compare the performance of various models, including fine-tuned multilingual models, LLMs (GPT-SW3, Llama 3.1), and closed-source models (Claude 3.5, GPT-4). Our findings show that a fine-tuned NLLB model outperforms most LLMs, including some larger models, in both automatic and human evaluations. We also demonstrate the effectiveness of using LLM-generated synthetic data for fine-tuning. While closed-source models like Claude 3.5 perform best overall, the competitive performance of smaller, fine-tuned models suggests a more nuanced approach to low-resource machine translation. Our results highlight the potential of specialized multilingual models and the importance of language-specific knowledge. We discuss implications for resource allocation in low-resource settings and suggest future directions for improving low-resource machine translation, including targeted data creation and more comprehensive evaluation methodologies.

This study evaluates GPT-4’s English-to-Faroese translation capabilities, comparing it with multilingual models on FLORES-200 and Sprotin datasets. We propose a prompt optimization strategy using Semantic Textual Similarity (STS) to improve translation quality. Human evaluation confirms the effectiveness of STS-based few-shot example selection, though automated metrics fail to capture these improvements. Our findings advance LLM applications for low-resource language translation while highlighting the need for better evaluation methods in this context.

Mozilla Common Voice is a crowdsourced project that aims to create a public, multilingual dataset of voice recordings for training speech recognition models. In Common Voice, anyone can contribute by donating or validating recordings in various languages. However, despite the availability of many recordings in certain languages, a significant percentage remains unvalidated by users. This is the case for Spanish, where in version 17.0 of Common Voice, 75% of the 2,220 hours of recordings are unvalidated. In this work, we used the Whisper recognizer to automatically validate approximately 784 hours of recordings which are more than the 562 hours validated by users. To verify the accuracy of the validation, we developed a speech recognition model based on a version of NVIDIA-NeMo’s Parakeet, which does not have an official Spanish version. Our final model achieved a WER of less than 4% on the test and validation splits of Common Voice 17.0. Both the model and the speech corpus are publicly available on Hugging Face.

We investigate GPT-SW3, a generative language model for the Nordic languages, to assess its understanding of the low-resourced Faroese language. Our aim is to demonstrate the advantages of using language-family-specific generative models to augment data for related languages with fewer resources. We evaluate GPT-SW3 by prompting it for Faroese to English translation in a zero, one, and few-shot setting. We assess such translations with an ensemble score consisting of an arithmetic average between the BLEU and a semantic similarity score (SBERT). Moreover, we challenge the model’s Faroese language understanding capabilities on a small dataset of curated Faroese trick sentences. There, we make a qualitative comparison of the model’s performance with respect to Open AI’s GPT-3.5 and GPT-4, demonstrating the advantages of using a language-family-specific generative model for navigating non-trivial scenarios. We evaluate the pipeline thus created and use it, as a proof of concept, to create an automatically annotated Faroese semantic textual similarity (STS) dataset.