Applying automatic speech recognition to Indigenous language documentation: A case study with Hul’q’umi’num’
Date
2026
Authors
Jiang, Xin He
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Abstract
The process of documenting Indigenous languages can create a large amount of audio recordings that are difficult to convert into a written form. Speeding up the transcription process using automatic speech recognition could help the Hul’q’umi’num’ Language & Culture Society to create pedagogical materials and make their recordings more accessible. In this project, I trained a language model known as XLS-R on Hul’q’umi’num’ audio recordings to determine how accurately it can transcribe Hul’q’umi’num’, whether particular linguistic and orthographic features are more difficult for XLS-R to transcribe, and what amount of time and computational resources the training takes. The model reached a CER of 11.1% and WER of 50% using 26 minutes of continuous speech. Most phonemes could be transcribed with high accuracy but the model showed difficulties with segmenting words, differentiating glottalized consonants from plain consonants, determining vowel length, and predicting the placement of glottal stops.
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Keywords
automatic speech recognition, Hul'q'umi'num', linguistics, transcription, error analysis, Indigenous