Doing More with Less: Determining Optimal Pre-training Model for Irish Automatic Speech Recognition through Multi-step Fine-tuning

Abstract

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.

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