  Many archival recordings of speech from endangered languages remain
unannotated and inaccessible to community members and language learning
programs. One bottleneck is the time-intensive nature of annotation. An even
narrower bottleneck occurs for recordings with access constraints, such as
language that must be vetted or filtered by authorised community members before
annotation can begin. We propose a privacy-preserving workflow to widen both
bottlenecks for recordings where speech in the endangered language is
intermixed with a more widely-used language such as English for meta-linguistic
commentary and questions (e.g. What is the word for 'tree'?). We integrate
voice activity detection (VAD), spoken language identification (SLI), and
automatic speech recognition (ASR) to transcribe the metalinguistic content,
which an authorised person can quickly scan to triage recordings that can be
annotated by people with lower levels of access. We report work-in-progress
processing 136 hours archival audio containing a mix of English and Muruwari.
Our collaborative work with the Muruwari custodian of the archival materials
show that this workflow reduces metalanguage transcription time by 20% even
given only minimal amounts of annotated training data: 10 utterances per
language for SLI and for ASR at most 39 minutes, and possibly as little as 39
seconds.
