  We present improvements in automatic speech recognition (ASR) for Somali, a
currently extremely under-resourced language. This forms part of a continuing
United Nations (UN) effort to employ ASR-based keyword spotting systems to
support humanitarian relief programmes in rural Africa. Using just 1.57 hours
of annotated speech data as a seed corpus, we increase the pool of training
data by applying semi-supervised training to 17.55 hours of untranscribed
speech. We make use of factorised time-delay neural networks (TDNN-F) for
acoustic modelling, since these have recently been shown to be effective in
resource-scarce situations. Three semi-supervised training passes were
performed, where the decoded output from each pass was used for acoustic model
training in the subsequent pass. The automatic transcriptions from the best
performing pass were used for language model augmentation. To ensure the
quality of automatic transcriptions, decoder confidence is used as a threshold.
The acoustic and language models obtained from the semi-supervised approach
show significant improvement in terms of WER and perplexity compared to the
baseline. Incorporating the automatically generated transcriptions yields a
6.55\% improvement in language model perplexity. The use of 17.55 hour of
Somali acoustic data in semi-supervised training shows an improvement of 7.74\%
relative over the baseline.
