Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan
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 ch...
| Authors | Chihiro Taguchi, Yukinori Takubo, David Chiang |
|---|---|
| Primary category | cs.CL |
| Published | 27 March 2026 |
| Last updated at source | 20 April 2026 |
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