  Our study presents a series of experiments on speech recognition with
endangered and extinct Samoyedic languages, spoken in Northern and Southern
Siberia. To best of our knowledge, this is the first time a functional ASR
system is built for an extinct language. We achieve with Kamas language a Label
Error Rate of 15\%, and conclude through careful error analysis that this
quality is already very useful as a starting point for refined human
transcriptions. Our results with related Nganasan language are more modest,
with best model having the error rate of 33\%. We show, however, through
experiments where Kamas training data is enlarged incrementally, that Nganasan
results are in line with what is expected under low-resource circumstances of
the language. Based on this, we provide recommendations for scenarios in which
further language documentation or archive processing activities could benefit
from modern ASR technology. All training data and processing scripts haven been
published on Zenodo with clear licences to ensure further work in this
important topic.
