L2-ARCTIC: A Non-Native English Speech Corpus

被引：59

作者：

Zhao, Guanlong ^{[1
]}

Sonsaat, Sinem ^{[2
]}

Silpachai, Alif ^{[2
]}

Lucic, Ivana ^{[2
]}

Chukharev-Hudilainen, Evgeny ^{[2
]}

Levis, John ^{[2
]}

Gutierrez-Osuna, Ricardo ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX 77843 USA

[2] Iowa State Univ, Dept English, Ames, IA USA

来源：

19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES | 2018年

关键词：

speech corpus; voice conversion; accent conversion; mispronunciation detection; STRESS;

D O I：

10.21437/Interspeech.2018-1110

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce L2-ARCTIC, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection. This initial release includes recordings from ten non-native speakers of English whose first languages (LIs) are Hindi, Korean, Mandarin, Spanish, and Arabic, each L1 containing recordings from one male and one female speaker. Each speaker recorded approximately one hour of read speech from the Carnegie Mellon University ARCTIC prompts, from which we generated orthographic and forced-aligned phonetic transcriptions. In addition, we manually annotated 150 utterances per speaker to identify three types of mispronunciation errors: substitutions, deletions, and additions, making it a valuable resource not only for research in voice conversion and accent conversion but also in computer-assisted pronunciation training. The corpus is publicly accessible at https://psi.engr.tamu.edu/12-arctic-corpus/.

引用

页码：2783 / 2787

页数：5

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