  We propose a novel transcription workflow which combines spoken term
detection and human-in-the-loop, together with a pilot experiment. This work is
grounded in an almost zero-resource scenario where only a few terms have so far
been identified, involving two endangered languages. We show that in the early
stages of transcription, when the available data is insufficient to train a
robust ASR system, it is possible to take advantage of the transcription of a
small number of isolated words in order to bootstrap the transcription of a
speech collection.
