  This paper reports on the semi-supervised development of acoustic and
language models for under-resourced, code-switched speech in five South African
languages. Two approaches are considered. The first constructs four separate
bilingual automatic speech recognisers (ASRs) corresponding to four different
language pairs between which speakers switch frequently. The second uses a
single, unified, five-lingual ASR system that represents all the languages
(English, isiZulu, isiXhosa, Setswana and Sesotho). We evaluate the
effectiveness of these two approaches when used to add additional data to our
extremely sparse training sets. Results indicate that batch-wise
semi-supervised training yields better results than a non-batch-wise approach.
Furthermore, while the separate bilingual systems achieved better recognition
performance than the unified system, they benefited more from pseudo-labels
generated by the five-lingual system than from those generated by the bilingual
systems.
