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

被引：0

作者：

Lovenia, Holy ^{[1
]}

Cahyawijaya, Samuel ^{[1
]}

Winata, Genta Indra ^{[1
]}

Xu, Peng ^{[1
]}

Yan, Xu ^{[1
]}

Liu, Zihan ^{[1
]}

Frieske, Rita ^{[1
]}

Yu, Tiezheng ^{[1
]}

Dai, Wenliang ^{[1
]}

Barezi, Elham J. ^{[1
]}

Chen, Qifeng ^{[1
]}

Ma, Xiaojuan ^{[1
]}

Shi, Bertram E. ^{[1
]}

Fung, Pascale ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China

来源：

LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2022年

关键词：

code-switching; corpus; bilingual; speech; dialogue; Mandarin Chinese; English; low-resource; SPEECH; READ;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：7259 / 7268

页数：10

共 46 条

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