Linguistically Informed Tokenization Improves ASR for Underresourced Languages

• URL: http://arxiv.org/abs/2510.06461v1
• Date: Tue, 07 Oct 2025 20:54:35 GMT
• Title: Linguistically Informed Tokenization Improves ASR for Underresourced Languages
• Authors: Massimo Daul, Alessio Tosolini, Claire Bowern,
• Abstract summary: We fine-tune a wav2vec2 ASR model on Yan-nhangu, a dormant Indigenous Australian language.<n>A linguistically informed phonemic tokenization system substantially improves WER and CER.<n>Hand-correcting the output of an ASR model is much faster than hand-transcribing audio from scratch, demonstrating that ASR can work for underresourced languages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic speech recognition (ASR) is a crucial tool for linguists aiming to perform a variety of language documentation tasks. However, modern ASR systems use data-hungry transformer architectures, rendering them generally unusable for underresourced languages. We fine-tune a wav2vec2 ASR model on Yan-nhangu, a dormant Indigenous Australian language, comparing the effects of phonemic and orthographic tokenization strategies on performance. In parallel, we explore ASR's viability as a tool in a language documentation pipeline. We find that a linguistically informed phonemic tokenization system substantially improves WER and CER compared to a baseline orthographic tokenization scheme. Finally, we show that hand-correcting the output of an ASR model is much faster than hand-transcribing audio from scratch, demonstrating that ASR can work for underresourced languages.

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