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1@article{NOWAKOWSKI2023103148,
2title = {Adapting multilingual speech representation model for a new, underresourced language through multilingual fine-tuning and continued pretraining},
3journal = {Information Processing & Management},
4volume = {60},
5number = {2},
6pages = {103148},
7year = {2023},
8issn = {0306-4573},
9doi = {https://doi.org/10.1016/j.ipm.2022.103148},
10url = {https://www.sciencedirect.com/science/article/pii/S0306457322002497},
11author = {Karol Nowakowski and Michal Ptaszynski and Kyoko Murasaki and Jagna Nieuważny},
12keywords = {Automatic speech transcription, ASR, Wav2vec 2.0, Pretrained transformer models, Speech representation models, Cross-lingual transfer, Language documentation, Endangered languages, Underresourced languages, Sakhalin Ainu},
13abstract = {In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model’s performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.}
14}