发表机构
Centre of Sustainability Research, University of Otago; Te Pūnaha Matatini Centre of Research Excellence for Complex Systems; New Zealand Chinese Association（奥塔哥大学可持续发展研究中心; Te Pūnaha Matatini 复杂系统卓越研究中心; 新西兰华人协会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对新西兰粤语口述历史，测试Whisper等ASR工具包在语码转换场景的有效性，发现其虽存在非英语片段转录问题，但能大幅减少转录时间，为多语种口述历史研究提供实用工具。
AI 中文摘要
本文从独特视角探讨语音技术如何被社区主导的遗产语言保护与振兴计划采用。作为社区主导的语言维护策略，口述历史在新西兰粤语振兴中发挥关键作用。自动语音识别（ASR）工具包（如Whisper）的开发，大幅加快了原本资源密集且耗时的口述历史合集转录流程。然而，针对ASR工具包在语码转换语言场景下的有效性研究有限。基于词错误率（WER），性能最优的Whisper模型配置实现了12.10的WER，但代价是无法准确转录不受支持的非英语片段。不过，Whisper仅用人工转录预估所需时间的1%就能提供初版转录，仍是实用工具。
英文摘要
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.
CommentsThis preprint reflects an updated version of the manuscript prepared for Interspeech 2026, incorporating revisions based on reviewer feedback
