Automatic Speech Recognition for Multilingual Oral History Research
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2609.04232 (eess)
[Submitted on 21 Jul 2026]
Title:Automatic Speech Recognition for Multilingual Oral History Research
View a PDF of the paper titled Automatic Speech Recognition for Multilingual Oral History Research, by Sidney Wong and 5 other authors
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Abstract:This paper offers a unique perspective on how speech technologies are being adopted by community-led heritage language preservation and revitalisation initiatives. As a community-led language maintenance strategy, oral histories play a crucial role in Cantonese language revitalisation in New Zealand. The development of Automatic Speech Recognition (ASR) toolkits, such as Whisper, have expedited what has often been a resource and time-intensive process of transcribing oral history collections. However, there is limited research into the effectiveness of ASR toolkits when applied to code-switched language contexts. Based on Word Error Rate (WER), the best performing Whisper model configuration achieved a WER of 12.10 at the expense of accurately transcribing unsupported non-English segments. However, Whisper remains a useful tool by providing a first-pass transcription using only 1% of the estimated time otherwise needed for manual transcription.
| Comments: | This preprint reflects an updated version of the manuscript prepared for Interspeech 2026, incorporating revisions based on reviewer feedback |
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.04232 [eess.AS] |
| (or arXiv:2609.04232v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04232
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Automatic Speech Recognition for Multilingual Oral History Research, by Sidney Wong and 5 other authors
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