Improving Luxembourgish Speech Recognition with Cross-Lingual Speech Representations
| Autor: | Le Minh Nguyen, Shekhar Nayak, Matt Coler |
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| Přispěvatelé: | Culture, Language & Technology |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | 2022 IEEE Spoken Language Technology Workshop, SLT 2022-Proceedings, 792-797 STARTPAGE=792;ENDPAGE=797;TITLE=2022 IEEE Spoken Language Technology Workshop, SLT 2022-Proceedings |
| Popis: | Luxembourgish is a West Germanic language spoken by roughly 390,000 people, mainly in Luxembourg. It is one of Europe's under-described and under-resourced languages, not extensively investigated in the context of speech recognition. We explore the self-supervised multilingual learning of Luxembourgish speech representations for the speech recognition downstream task. We show that learning cross-lingual representations is essential for low-resourced languages such as Luxembourgish. Learning cross-lingual representations and rescoring the output transcriptions with language modelling while using only 4 hours of labelled speech achieves a word error rate of 15.1% and improves our Transfer Learning baseline model relatively by 33.1% and absolutely by 7.5%. Increasing the amount of labelled speech to 14 hours yields a significant performance gain resulting in a 9.3% word error rate.11Models and datasets are available at https://hugging£ace.co/lemswasabi |
| Databáze: | OpenAIRE |
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