From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition
Dhivehi, the national language of the Maldives, is currently under-resourced for automatic speech recognition (ASR) and other NLP tasks. This study investigates whether cross-lingual transfer learning from Sinhala, a linguistically related, relatively well-resourced Insular Indo-Aryan language, can improve Dhivehi ASR. We conduct seventeen experiments across five transfer learning paradigms: Dhivehi-only baselines, sequential fine-tuning, multilingual fine-tuning, continual pre-training, and a control using Turkish as an unrelated language. The strongest system, continual pre-training on Sinha
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- PossiblePossibly related (embedding) · 47%PacificAI/langtest →
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- LinkedLinked via arxiv author · 85%Lukmal Ilyas →
“From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition”
- LinkedLinked via arxiv author · 85%Nevidu Jayatilleke →
“From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for Low-Resource Speech Recognition”
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“Fuzzy title match (0.94): “From Sinhala to Dhivehi: Cross-Lingual Transfer Learning for” ≈ “huggingface/speech-to-speech””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
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