  Automatic speech recognition (ASR) systems often need to be developed for
extremely low-resource languages to serve end-uses such as audio content
categorization and search. While universal phone recognition is natural to
consider when no transcribed speech is available to train an ASR system in a
language, adapting universal phone models using very small amounts (minutes
rather than hours) of transcribed speech also needs to be studied, particularly
with state-of-the-art DNN-based acoustic models. The DARPA LORELEI program
provides a framework for such very-low-resource ASR studies, and provides an
extrinsic metric for evaluating ASR performance in a humanitarian assistance,
disaster relief setting. This paper presents our Kaldi-based systems for the
program, which employ a universal phone modeling approach to ASR, and describes
recipes for very rapid adaptation of this universal ASR system. The results we
obtain significantly outperform results obtained by many competing approaches
on the NIST LoReHLT 2017 Evaluation datasets.
