  We present an analysis of semi-supervised acoustic and language model
training for English-isiZulu code-switched ASR using soap opera speech.
Approximately 11 hours of untranscribed multilingual speech was transcribed
automatically using four bilingual code-switching transcription systems
operating in English-isiZulu, English-isiXhosa, English-Setswana and
English-Sesotho. These transcriptions were incorporated into the acoustic and
language model training sets. Results showed that the TDNN-F acoustic models
benefit from the additional semi-supervised data and that even better
performance could be achieved by including additional CNN layers. Using these
CNN-TDNN-F acoustic models, a first iteration of semi-supervised training
achieved an absolute mixed-language WER reduction of 3.4%, and a further 2.2%
after a second iteration. Although the languages in the untranscribed data were
unknown, the best results were obtained when all automatically transcribed data
was used for training and not just the utterances classified as
English-isiZulu. Despite reducing perplexity, the semi-supervised language
model was not able to improve the ASR performance.
