  This paper presents recent progress in the acoustic modelling of
under-resourced code-switched (CS) speech in multiple South African languages.
We consider two approaches. The first constructs separate bilingual acoustic
models corresponding to language pairs (English-isiZulu, English-isiXhosa,
English-Setswana and English-Sesotho). The second constructs a single unified
five-lingual acoustic model representing all the languages (English, isiZulu,
isiXhosa, Setswana and Sesotho). For these two approaches we consider the
effectiveness of semi-supervised training to increase the size of the very
sparse acoustic training sets. Using approximately 11 hours of untranscribed
speech, we show that both approaches benefit from semi-supervised training. The
bilingual TDNN-F acoustic models also benefit from the addition of CNN layers
(CNN-TDNN-F), while the five-lingual system does not show any significant
improvement. Furthermore, because English is common to all language pairs in
our data, it dominates when training a unified language model, leading to
improved English ASR performance at the expense of the other languages.
Nevertheless, the five-lingual model offers flexibility because it can process
more than two languages simultaneously, and is therefore an attractive option
as an automatic transcription system in a semi-supervised training pipeline.
