  We present our first efforts towards building a single multilingual automatic
speech recognition (ASR) system that can process code-switching (CS) speech in
five languages spoken within the same population. This contrasts with related
prior work which focuses on the recognition of CS speech in bilingual
scenarios. Recently, we have compiled a small five-language corpus of South
African soap opera speech which contains examples of CS between 5 languages
occurring in various contexts such as using English as the matrix language and
switching to other indigenous languages. The ASR system presented in this work
is trained on 4 corpora containing English-isiZulu, English-isiXhosa,
English-Setswana and English-Sesotho CS speech. The interpolation of multiple
language models trained on these language pairs enables the ASR system to
hypothesize mixed word sequences from these 5 languages. We evaluate various
state-of-the-art acoustic models trained on this 5-lingual training data and
report ASR accuracy and language recognition performance on the development and
test sets of the South African multilingual soap opera corpus.
