  The lack of code-switch training data is one of the major concerns in the
development of end-to-end code-switching automatic speech recognition (ASR)
models. In this work, we propose a method to train an improved end-to-end
code-switching ASR using only monolingual data. Our method encourages the
distributions of output token embeddings of monolingual languages to be
similar, and hence, promotes the ASR model to easily code-switch between
languages. Specifically, we propose to use Jensen-Shannon divergence and cosine
distance based constraints. The former will enforce output embeddings of
monolingual languages to possess similar distributions, while the later simply
brings the centroids of two distributions to be close to each other.
Experimental results demonstrate high effectiveness of the proposed method,
yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English
code-switching ASR task.
