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Code-switching (CS) is a common phenomenon and recognizing CS speech ischallenging. But CS speech data is scarce and there' s no common testbed inrelevant research. This paper describes the design and main outcomes of theASRU 2019 Mandarin-English code-switching speech recognition challenge, whichaims to improve the ASR performance in Mandarin-English code-switchingsituation. 500 hours Mandarin speech data and 240 hours Mandarin-Englishintra-sentencial CS data are released to the participants. Three tracks wereset for advancing the AM and LM part in traditional DNN-HMM ASR system, as wellas exploring the E2E models' performance. The paper then presents an overviewof the results and system performance in the three tracks. It turns out thattraditional ASR system benefits from pronunciation lexicon, CS text generatingand data augmentation. In E2E track, however, the results highlight theimportance of using language identification, building-up a rational set ofmodeling units and spec-augment. The other details in model training and methodcomparsion are discussed.

The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results