推进低资源加纳语言的自动语音识别:阿肯语,埃维语,达格巴尼语,达加雷语和伊克波索语的音频数据集
Isaac Wiafe1, Jamal-Deen Abdulai1, Akon Obu Ekpezu1
1Department of Computer Science, University of Ghana, Legon-Accra, Ghana.
Data in brief
|July 29, 2025
概括
本研究介绍了加纳和多哥的五种低资源语言的5000小时音频语音数据集,以帮助自动语音识别 (ASR) 的发展. 该库包括转录,支持语言研究和代表性不足的语言的技术进步.
科学领域:
- 计算语言学 计算语言学
- 语音技术 语言技术
- 非洲语言 非洲语言
背景情况:
- 自动语音识别 (ASR) 的发展受到低资源语言 (LRL) 缺乏大规模音频数据集的阻碍.
- 现有的语音体对于许多土著语言来说是不够的,特别是在西非等地区.
- 弥合技术差距需要为LRLs提供专门的资源.
研究的目的:
- 为五个LRL引入大量的音频语音数据集:阿肯语,埃维语,达格巴尼语,达加雷语和Ikposo语.
- 创建一个有价值的资源,以推进这些语言的语音和语言技术.
- 支持加纳和多哥语言的保存和文献.
主要方法:
- 收集了5000小时的经过验证的音频演讲,来自阿肯,伊维,达格巴尼,达加雷和伊克波索的土著发言人.
- 录音包括每种语言的1000个文化相关图像的口头描述,通过自定义的Android移动应用程序捕获.
- 转录了10%的录音 (大约. 每种语言100小时) 以提高ASR和语言研究实用性.
主要成果:
- 在五个LRL中开发了5000小时的语音库,每个语言有1000小时.
- 为每个语言生成大约100小时的转录数据.
- 建立了一个基础数据集,用于这些语言的未来研究和技术开发.
结论:
- 新创建的音频数据集是低资源语言语音技术开发的关键资源.
- 这一举措代表了减少高资源和低资源语言技术之间的差异的重要一步.
- 保持道德数据收集实践,并为参与者提供激励措施.
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