  We present our first efforts in building an automatic speech recognition
system for Somali, an under-resourced language, using 1.57 hrs of annotated
speech for acoustic model training. The system is part of an ongoing effort by
the United Nations (UN) to implement keyword spotting systems supporting
humanitarian relief programmes in parts of Africa where languages are severely
under-resourced. We evaluate several types of acoustic model, including recent
neural architectures. Language model data augmentation using a combination of
recurrent neural networks (RNN) and long short-term memory neural networks
(LSTMs) as well as the perturbation of acoustic data are also considered. We
find that both types of data augmentation are beneficial to performance, with
our best system using a combination of convolutional neural networks (CNNs),
time-delay neural networks (TDNNs) and bi-directional long short term memory
(BLSTMs) to achieve a word error rate of 53.75%.
