In recent years, there has been an upsurge in research on automatic speech recognition (ASR) for low-resource languages. Particularly, transfer learning using multi-lingual models has become a popular remedy for the lack of available datasets for target languages. However, given the complexities associated with each individual language, we argue it is unlikely that a single multi-lingual pre-training model will provide equal performance gains across all languages. We also recognise the important, and insufficiently studied influence that the specific pre-training dataset has on the performance of the model. In this paper, using the Irish language as a case study, we propose a more directed, incremental form of pre-training which we term multi-step fine-tuning. This method accounts for the complex relationships between the language and dataset features of the source pre-training and target datasets. We show multi-step fine-tuning improves performance over simple multi-lingual fine-tuning alone, and we investigate factors leading to certain pre-trained models achieving better results through linguistic and dataset similarity measures. This research also investigates the uniformity of the performance gains across different demographics. We show that the optimal pre-training strategy can differ between demographics suggesting that more careful pre-training dataset selection is necessary to ensure equitable outcomes in practice.

The value of high-quality datasets for training essential language tools has long been recognised for NLP research. Despite the importance of such datasets, most language data available for training consists of large, automatically curated corpora, often scraped from web content. The quality of such datasets is often an unknown factor. This presents a problem for already low-resourced languages (such as Irish), as existing datasets may not provide adequate, representative language data for training effective models. This paper examines existing monolingual and parallel Irish text corpora to evaluate the quality of the language data, through manual review, automatic metrics, and LLMs as judges.

Pose estimation keypoints are widely used in sign language recognition (SLR) as a means of generalising to unseen signers. Despite the advantages of keypoints, SLR models struggle to achieve high recognition accuracy for many signed languages due to the large degree of variability between occurrences of the same signs, the lack of large datasets and the imbalanced nature of the data therein. In this paper we seek to provide a deeper analysis into the ways that these keypoints are used by models in order to determine which are most informative to SLR, identify potentially redundant ones and investigate whether keypoints that are central to differentiating signs in practice are being effectively used as expected by models.

The reliance of deep learning algorithms on large scale datasets represents a significant challenge when learning from low resource sign language datasets. This challenge is compounded when we consider that, for a model to be effective in the real world, it must not only learn the variations of a given sign, but also learn to be invariant to the person signing. In this paper, we first illustrate the performance gap between signer-independent and signer-dependent models on Irish Sign Language manual hand shape data. We then evaluate the effect of transfer learning, with different levels of fine-tuning, on the generalisation of signer independent models, and show the effects of different input representations, namely variations in image data and pose estimation. We go on to investigate the sensitivity of current pose estimation models in order to establish their limitations and areas in need of improvement. The results show that accurate pose estimation outperforms raw RGB image data, even when relying on pre-trained image models. Following on from this, we investigate image texture as a potential contributing factor to the gap in performance between signer-dependent and signer-independent models using counterfactual testing images and discuss potential ramifications for low-resource sign languages. Keywords: Sign language recognition, Transfer learning, Irish Sign Language, Low-resource languages