Linguistically Informed Tokenization Improves ASR for Underresourced Languages
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 7 Oct 2025.
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9 of 31 references.
References · 9
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 347
- 2020Byte Pair Encoding is Suboptimal for Language Model Pretrainingcited by 192
- 2021Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Wordscited by 63
- 2024MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modelingcited by 40
- 2023Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretrainingcited by 18
- 2024Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahilicited by 4
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