This paper presents three experiments to test the most effective and efficient ASR pipeline to facilitate the documentation and preservation of endangered languages, which are often extremely low-resourced. With data from two languages in Nepal —Dzardzongke and Newar— we show that model improvements are different for different masses of data, and that transfer learning as well as a range of modifications (e.g. normalising amplitude and pitch) can be effective, but that a consistently-standardised orthography as NLP input and post-training dictionary corrections improve results even more.