VanuatuASR: Open Multilingual Speech Recognition for Low-Resource Vanuatu Languages

Multilingual automatic speech recognition (ASR) remains inaccessible to many languages that lack transcribed speech, digital text, and representation in large-scale pretraining corpora. We present VanuatuASR, to our knowledge the first unified multilingual ASR system for Vanuatu, a highly linguistically diverse region where speech and text resources are severely limited. Building on speech curated across seven Vanuatu languages totaling 65.92 hours, we investigate three practical questions: whether joint multilingual fine-tuning can match language-specific recognizers; whether a shared language model trained on pooled transcripts improves decoding when per-language text is too scarce to support individual models; and whether adding untranscribed speech from additional languages benefits self-supervised representation learning. Our system combines SSL on pooled unlabeled speech, language-balanced joint CTC fine-tuning on four transcribed languages, and shared character $n$-gram LM decoding without language identifiers. Our results show that joint training matches four language-specific models with a single recognizer. The shared LM is the dominant source of improvement, reducing macro WER from 37.37% to 32.12% and remaining effective with as little as 25% of transcribed training data. Expanding the SSL pool with untranscribed speech from three additional languages, however, yields no measurable gain. VanuatuASR outperforms other state-of-the-art baselines under a common evaluation protocol, reducing macro WER by 10.89% relative to the strongest baseline. These results indicate that in severely low-resource settings, transcribing existing recordings and pooling scarce textual resources across languages delivers more value than collecting additional unlabeled speech.