  An increasing number of people in the world today speak a mixed-language as a
result of being multilingual. However, building a speech recognition system for
code-switching remains difficult due to the availability of limited resources
and the expense and significant effort required to collect mixed-language data.
We therefore propose a new learning method, meta-transfer learning, to transfer
learn on a code-switched speech recognition system in a low-resource setting by
judiciously extracting information from high-resource monolingual datasets. Our
model learns to recognize individual languages, and transfer them so as to
better recognize mixed-language speech by conditioning the optimization on the
code-switching data. Based on experimental results, our model outperforms
existing baselines on speech recognition and language modeling tasks, and is
faster to converge.
