  Code-switching (CS) is a common phenomenon and recognizing CS speech is
challenging. But CS speech data is scarce and there' s no common testbed in
relevant research. This paper describes the design and main outcomes of the
ASRU 2019 Mandarin-English code-switching speech recognition challenge, which
aims to improve the ASR performance in Mandarin-English code-switching
situation. 500 hours Mandarin speech data and 240 hours Mandarin-English
intra-sentencial CS data are released to the participants. Three tracks were
set for advancing the AM and LM part in traditional DNN-HMM ASR system, as well
as exploring the E2E models' performance. The paper then presents an overview
of the results and system performance in the three tracks. It turns out that
traditional ASR system benefits from pronunciation lexicon, CS text generating
and data augmentation. In E2E track, however, the results highlight the
importance of using language identification, building-up a rational set of
modeling units and spec-augment. The other details in model training and method
comparsion are discussed.
