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

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arxiv 2007.05916 v1 pith:EQMZDJCH submitted 2020-07-12 eess.AS cs.CLcs.SD

Xian Shi , Qiangze Feng , Lei Xie This is my paper
classification eess.AScs.CLcs.SD
keywords speechcode-switchingdatamandarin-englishperformanceresultssystemtracks
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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  4. Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs

    cs.CL 2026-06 unverdicted novelty 5.0 of 10

    Merged bilingual CS-ASR models show only modest generalization to unseen language pairs, indicating limited transfer of code-switching capabilities.

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