  "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.
