  In this paper, we investigate the code-switching detection performance of a
code-switching (CS) automatic speech recognition (ASR) system with
data-augmented acoustic and language models. We focus on the recognition of
Frisian-Dutch radio broadcasts where one of the mixed languages, namely
Frisian, is under-resourced. Recently, we have explored how the acoustic
modeling (AM) can benefit from monolingual speech data belonging to the
high-resourced mixed language. For this purpose, we have trained
state-of-the-art AMs on a significantly increased amount of CS speech by
applying automatic transcription and monolingual Dutch speech. Moreover, we
have improved the language model (LM) by creating CS text in various ways
including text generation using recurrent LMs trained on existing CS text.
Motivated by the significantly improved CS ASR performance, we delve into the
CS detection performance of the same ASR system in this work by reporting CS
detection accuracies together with a detailed detection error analysis.
