  In this paper, we present our overall efforts to improve the performance of a
code-switching speech recognition system using semi-supervised training methods
from lexicon learning to acoustic modeling, on the South East Asian
Mandarin-English (SEAME) data. We first investigate semi-supervised lexicon
learning approach to adapt the canonical lexicon, which is meant to alleviate
the heavily accented pronunciation issue within the code-switching conversation
of the local area. As a result, the learned lexicon yields improved
performance. Furthermore, we attempt to use semi-supervised training to deal
with those transcriptions that are highly mismatched between human transcribers
and ASR system. Specifically, we conduct semi-supervised training assuming
those poorly transcribed data as unsupervised data. We found the
semi-supervised acoustic modeling can lead to improved results. Finally, to
make up for the limitation of the conventional n-gram language models due to
data sparsity issue, we perform lattice rescoring using neural network language
models, and significant WER reduction is obtained.
