Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation

arXiv:2606.17820v1 Announce Type: new Abstract: This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographically diverse language pairs, covering a range of language families and writing systems. To distinguish the two languages, during training, we pre-pend each input text with a language identification token. At inference, the model jointly predicts both the language and transcription from the speech input alone. As texts for which the language is incorrectly determined sh

Source: arXiv cs.CL — read the full report at the original publisher.

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