Linguistically Informed Tokenization Improves ASR for Underresourced Languages
Abstract
AbstractAutomatic speech recognition (ASR) is a crucial tool for linguists aiming to perform a variety of language documentation tasks. However, modern ASR systems rely on data-hungry transformer architectures, rendering them generally unusable for underresourced languages. We fine-tune a wav2vec 2.0 ASR model on Yanyhangu, an 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 word error rate (WER) and character error rate (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 provide significant assistance for underresourced language documentation.