Automatic Speech Recognition for Multilingual Oral History Research

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.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)

arXiv:2609.04232 [eess.AS]

(or arXiv:2609.04232v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.04232 Focus to learn more arXiv-issued DOI via DataCite

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