  Modeling code-switched speech is an important problem in automatic speech
recognition (ASR). Labeled code-switched data are rare, so monolingual data are
often used to model code-switched speech. These monolingual data may be more
closely matched to one of the languages in the code-switch pair. We show that
such asymmetry can bias prediction toward the better-matched language and
degrade overall model performance. To address this issue, we propose a
semi-supervised approach for code-switched ASR. We consider the case of
English-Mandarin code-switching, and the problem of using monolingual data to
build bilingual "transcription models'' for annotation of unlabeled
code-switched data. We first build multiple transcription models so that their
individual predictions are variously biased toward either English or Mandarin.
We then combine these biased transcriptions using confidence-based selection.
This strategy generates a superior transcript for semi-supervised training, and
obtains a 19% relative improvement compared to a semi-supervised system that
relies on a transcription model built with only the best-matched monolingual
data.
