"Transcription bottlenecks", created by a shortage of effective human
transcribers are one of the main challenges to endangered language (EL)
documentation. Automatic speech recognition (ASR) has been suggested as a tool
to overcome such bottlenecks. Following this suggestion, we investigated the
effectiveness for EL documentation of end-to-end ASR, which unlike Hidden
Markov Model ASR systems, eschews linguistic resources but is instead more
dependent on large-data settings. We open source a Yolox\'ochitl Mixtec EL
corpus. First, we review our method in building an end-to-end ASR system in a
way that would be reproducible by the ASR community. We then propose a novice
transcription correction task and demonstrate how ASR systems and novice
transcribers can work together to improve EL documentation. We believe this
combinatory methodology would mitigate the transcription bottleneck and
transcriber shortage that hinders EL documentation.

研究使用自动语音识别作为一种工具，以解决由有效人工转录员短缺所导致的濒危语言文献记录中的转录瓶颈问题。提出了一种使用端到端 ASR 的方法，并提议了一项新手转录校正任务，以展示 ASR 系统和新手转录员如何共同改进濒危语言文献记录。该组合方法将缓解阻碍 EL 记录的转录瓶颈和转录员短缺。