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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 · ORCID
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 4 Pith papers

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  1. Context-Aware ASR for Mandarin Technical Lectures

    cs.SD 2026-07 conditional novelty 6.0 of 10

    Self-built lecture glossaries from first-pass ASR raise technical-term recall across five backbones while holding or lowering CER on a new Mandarin AI/ML lecture benchmark.

  2. OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia

    cs.SD 2025-01 conditional novelty 5.0 of 10

    An open, resource-lean speech understanding LLM trained on 50,500 hours matches or beats larger industry models on several Chinese benchmarks, with caveats in its internal evaluation.

  3. CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition

    cs.SD 2024-12 conditional novelty 5.0 of 10

    A cross-attention based mixture-of-experts architecture with language bias from a language diarization decoder improves Mandarin-English code-switching ASR, achieving state-of-the-art results on SEAME, ASRU200, and AS...

  4. Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection

    eess.AS 2024-11 conditional novelty 4.0 of 10

    Adding a language-identification block trained with non-peaky CTC and injecting the resulting language posteriors reduces mixed-error rate on Mandarin-English SEAME by about 0.5 to 0.8 percent absolute over the D-MoE ...

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