  Code-switching (CS), defined as the mixing of languages in conversations, has
become a worldwide phenomenon. The prevalence of CS has been recently met with
a growing demand and interest to build CS ASR systems. In this paper, we
present our work on code-switched Egyptian Arabic-English automatic speech
recognition (ASR). We first contribute in filling the huge gap in resources by
collecting, analyzing and publishing our spontaneous CS Egyptian Arabic-English
speech corpus. We build our ASR systems using DNN-based hybrid and
Transformer-based end-to-end models. In this paper, we present a thorough
comparison between both approaches under the setting of a low-resource,
orthographically unstandardized, and morphologically rich language pair. We
show that while both systems give comparable overall recognition results, each
system provides complementary sets of strength points. We show that recognition
can be improved by combining the outputs of both systems. We propose several
effective system combination approaches, where hypotheses of both systems are
merged on sentence- and word-levels. Our approaches result in overall WER
relative improvement of 4.7%, over a baseline performance of 32.1% WER. In the
case of intra-sentential CS sentences, we achieve WER relative improvement of
4.8%. Our best performing system achieves 30.6% WER on ArzEn test set.
