为濒危语言伊凯马语构建自动语音识别系统，显著提升转录效率。
Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan
- 基于实地录音构建6.33小时语料库，训练低错误率模型。
- 模型字符错误率低至15%，大幅减少人工转录时间。
- 适合语言保护者与跨学科研究者参考，推动技术赋能濒危语言。
语言濒危威胁全球语言多样性，技术进步为语言记录与复兴开辟新途径。自动语音识别（ASR）在濒危语言数据转录中展现出日益重要的潜力。本研究聚焦于日本冲绳的严重濒危语言伊凯马语，目前仅存约1,300名使用者，多数年龄超过60岁。我们开展一项持续的伊凯马语ASR系统开发工作，具体包括：(1) 基于实地录音构建6.33小时语音语料库；(2) 训练出字符错误率低至15%的ASR模型；(3) 评估ASR辅助对语音转录效率的影响。结果表明，集成ASR可显著降低转录耗时与认知负荷，为可持续、技术驱动的濒危语言记录提供可行路径。
原文摘要 · Abstract (English)
Language endangerment poses a major challenge to linguistic diversity worldwide, and technological advances have opened new avenues for documentation and revitalization. Among these, automatic speech recognition (ASR) has shown increasing potential to assist in the transcription of endangered language data. This study focuses on Ikema, a severely endangered Ryukyuan language spoken in Okinawa, Japan, with approximately 1,300 remaining speakers, most of whom are over 60 years old. We present an ongoing effort to develop an ASR system for Ikema based on field recordings. Specifically, we (1) construct a 6.33-hour speech corpus from field recordings, (2) train an ASR model that achieves a character error rate as low as 15%, and (3) evaluate the impact of ASR assistance on the efficiency of speech transcription. Our results demonstrate that ASR integration can substantially reduce transcription time and cognitive load, offering a practical pathway toward scalable, technology-supported documentation of endangered languages.
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