  Code-switching, also called code-mixing, is the linguistics phenomenon where
in casual settings, multilingual speakers mix words from different languages in
one utterance. Due to its spontaneous nature, code-switching is extremely
low-resource, which makes it a challenging problem for language and speech
processing tasks. In such contexts, Code-Switching Language Identification
(CSLID) becomes a difficult but necessary task if we want to maximally leverage
existing monolingual tools for other tasks. In this work, we propose two novel
approaches toward improving language identification accuracy on an
English-Mandarin child-directed speech dataset. Our methods include a stacked
Residual CNN+GRU model and a multitask pre-training approach to use Automatic
Speech Recognition (ASR) as an auxiliary task for CSLID. Due to the
low-resource nature of code-switching, we also employ careful silver data
creation using monolingual corpora in both languages and up-sampling as data
augmentation. We focus on English-Mandarin code-switched data, but our method
works on any language pair. Our best model achieves a balanced accuracy of
0.781 on a real English-Mandarin code-switching child-directed speech corpus
and outperforms the previous baseline by 55.3%.
