MDER-MA:一个多式联络数据集,用于在低资源的摩洛哥阿拉伯语言中识别情绪
Soufiyan Ouali1, Said El Garouani1
1Faculty of Sciences Dhar El Mahraz (FSDM), Sidi Mohamed Ben Abdellah University, LISAC laboratory, Fez, 30000, Morocco.
Data in brief
|September 16, 2025
概括
这项研究介绍了MDER-MA,这是一个新的多式联络数据集,用于在摩洛哥阿拉伯语中识别情绪. 该资源旨在改善在低资源语言中与虚拟代理进行的人类式交互的功能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 语音识别 语言识别
背景情况:
- 情感识别对于使虚拟代理人人性化和增强用户体验至关重要.
- 由于文化和语言差异,现有的研究对像摩洛哥阿拉伯语这样的低资源语言是有限的.
- 高质量,现实的数据集对于开发有效的情绪识别系统至关重要.
研究的目的:
- 介绍MDER-MA,这是一个全面的多式联络数据集,用于摩洛哥阿拉伯语的情感识别.
- 为解决低资源语言情感识别研究的差距.
- 促进阿拉伯语言技术的发展,特别是区域方言.
主要方法:
- 开发了MDER-MA数据集,其中包括四种情绪 (快乐,悲伤,愤怒,中立) 的5288个样本.
- 在四种方式收集数据:音频,文本,光谱图和Mel光谱图像.
- 通过收集来自摩洛哥各个地区的样本和使用母语人士进行注释来确保数据集的代表性.
主要成果:
- MDER-MA包含每种模式的1322个样本,共计5288个数据项.
- 该数据集支持超越情感识别的应用,包括音频转录和人口统计识别.
- 五位摩洛哥本地发言人的注释确保了高度的语言可靠性.
结论:
- MDER-MA弥合了摩洛哥阿拉伯语等低资源语言在情感识别方面的差距.
- 该数据集有助于创建更自然,更有同情心的虚拟代理.
- 这项工作促进了阿拉伯自然语言处理和情感感知AI的进步.
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