一个数据集,用于从口语L2英语演讲 (ArL2Eng) 中识别阿拉伯语口音
Manssour Habbash1, Sami Mnasri2, Mansoor Alghamdi3
1Applied College, University of Tabuk, Tabuk, 47512, Saudi Arabia. m_habbash@ut.edu.sa.
Scientific data
|July 31, 2025
概括
ArL2Eng数据集提供阿拉伯语使用者的英语演讲,帮助自动化语言评估和口音识别研究. 本资源支持为L2英语学习者开发客观流性评估模型.
科学领域:
- 计算语言学 计算语言学
- 语音处理 语音处理
- 语言评估语言评估
背景情况:
- 自动语言评估需要多样化的语音体.
- 现有的数据集可能缺乏特定的L2学习者数据,特别是对于阿拉伯语使用者学习英语.
- 准确的流利度指标和口音分析对于有效的语言学习工具至关重要.
研究的目的:
- 介绍ArL2Eng数据集,这是来自阿拉伯语母语者的L2英语的新型语音语料库.
- 促进自动化语言评估,口音识别和语音处理方面的研究.
- 为阿拉伯语使用者开发和验证客观英语流利评估模型提供资源.
主要方法:
- 收集并策划了一份由阿拉伯语母语者发言的英语发言语语库 (ArL2Eng).
- 标注了数据集的很大一部分与专家评级的流度指标.
- 使用Mel频率 Cepstral系数 (MFCCs) 进行声学和声学特征提取.
- 应用深度学习技术和维度减小,用于流利性预测和口音分析.
主要成果:
- ArL2Eng数据集包含640个音频记录,其中471个为流利性进行注释.
- 证明了数据集在预测阿拉伯口音发言者中英语流利性的实用性.
- 展示了口音分类和扬声器识别应用的潜力.
结论:
- ArL2Eng是一个有价值的,公开可用的资源,用于L2英语的研究人员和教育工作者.
- 该数据集支持开发可扩展和客观的模型来评估英语流性.
- 利用ArL2Eng进行进一步的研究可以为特定的学习群体推进自动化语言评估和语音技术.
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