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ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation

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arxiv 2112.06223 v6 pith:PHBVSZPQ submitted 2021-12-12 cs.CL

Holy Lovenia , Samuel Cahyawijaya , Genta Indra Winata , Peng Xu , Xu Yan , Zihan Liu , Rita Frieske , Tiezheng Yu
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Wenliang Dai Elham J. Barezi Qifeng Chen Xiaojuan Ma Bertram E. Shi Pascale Fung
This is my paper · ORCID
classification cs.CL
keywords code-switchingspeechspontaneousascendchinese-englishcollectedconversationconversational
verification ladder T0 review T1 audit T2 compute T3 formal

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Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND's design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69\% character error rate and 27.05% mixed error rate.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset

    cs.CL 2025-05 reject novelty 5.0 of 10

    The authors present SwitchLingua, a large multilingual code-switching text and audio dataset, and SAER, a semantic-aware error metric for code-switching ASR evaluation.

  2. InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions

    cs.CV 2024-12 conditional novelty 5.0 of 10

    The authors integrate streaming perception, compressed long-term memory, and a reasoning model into one open-source system, reporting SOTA open-source results on several video and audio benchmarks.

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