  We present first speech recognition systems for the two severely
under-resourced Malian languages Bambara and Maasina Fulfulde. These systems
will be used by the United Nations as part of a monitoring system to inform and
support humanitarian programmes in rural Africa. We have compiled datasets in
Bambara and Maasina Fulfulde, but since these are very small, we take advantage
of six similarly under-resourced datasets in other languages for multilingual
training. We focus specifically on the best composition of the multilingual
pool of speech data for multilingual training. We find that, although
maximising the training pool by including all six additional languages provides
improved speech recognition in both target languages, substantially better
performance can be achieved by a more judicious choice. Our experiments show
that the addition of just one language provides best performance. For Bambara,
this additional language is Maasina Fulfulde, and its introduction leads to a
relative word error rate reduction of 6.7%, as opposed to a 2.4% relative
reduction achieved when pooling all six additional languages. For the case of
Maasina Fulfulde, best performance was achieved when adding only Luganda,
leading to a relative word error rate improvement of 9.4% as opposed to a 3.9%
relative improvement when pooling all six languages. We conclude that careful
selection of the out-of-language data is worthwhile for multilingual training
even in highly under-resourced settings, and that the general assumption that
more data is better does not always hold.
