Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR

Jul 14, 2026

Researchers introduce a framework that combines acoustic and linguistic similarity measures to select source languages for cross-lingual automatic speech recognition (ASR) transfer to Warlpiri, an extremely low-resource Australian Aboriginal language. Using Whisper, they find that source languages with high acoustic and typological similarity, such as Assamese and Hindi, significantly reduce error rates compared to monolingual and multilingual baselines. The study also shows that acoustic similarity is the strongest predictor of fine-tuning performance, while phoneme inventory and typological similarity are more predictive for zero-shot transfer.

Why it matters: This work offers a systematic approach to improving ASR for endangered languages with minimal data, supporting efforts to preserve linguistic diversity.

Full story at: arXiv Audio and Speech Processing ↗