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.

中文翻译：

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ASRU 2019 普通话-英语代码切换语音识别挑战赛：开放数据集、轨道、方法和结果

代码切换 (CS) 是一种常见现象，识别 CS 语音具有挑战性。但 CS 语音数据稀缺，相关研究也没有通用的测试平台。本文描述了 ASRU 2019 普通话-英语代码转换语音识别挑战的设计和主要成果，旨在提高普通话-英语代码转换情况下的 ASR 性能。向参与者发布500小时普通话语音数据和240小时普通话-英语句内CS数据。设置了三个轨道用于推进传统 DNN-HMM ASR 系统中的 AM 和 LM 部分，以及探索 E2E 模型的性能。然后，本文概述了三个轨道中的结果和系统性能。事实证明，传统的 ASR 系统受益于发音词典，CS 文本生成和数据增强。然而，在 E2E 赛道中，结果强调了使用语言识别、建立一组合理的建模单元和规范扩充的重要性。讨论了模型训练和方法比较中的其他细节。