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孟加拉语MUSE:为语音和情感分析提供文本音频对的多式孟加拉语情感数据集
Md Darun Nayeem1, Zarin Rafa2, Tasnuva Tasnim Nova2
1Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Data in brief
|February 9, 2026
概括
一个新的多式联络孟加拉语情绪数据集为研究提供了对齐的文本和语音. 本资源支持孟加拉语的语音处理,情感分析和低资源语言建模方面的进展.
科学领域:
- 自然语言处理自然语言处理.
- 语音处理 语音处理
- 人工智能的人工智能
背景情况:
- 多模式数据集对于推进人工智能研究至关重要,特别是对于低资源语言.
- 现有的资源往往缺乏同步的语音和文本数据,以分析孟加拉语的情绪.
- 弥合这一差距对于开发包容性AI技术至关重要.
研究的目的:
- 为了引入一个新的,公开可用的多式联络孟加拉语情绪数据集.
- 促进情感分析,语音处理和低资源语言建模方面的研究.
- 支持开发能够理解微妙的人类情绪的AI系统.
主要方法:
- 在积极和消极情绪类别中策划了1000个孟加拉语句子.
- 收集了来自四位母语人士的4000个同步音频录音.
- 通过手动验证和控制的记录条件来确保数据质量.
主要成果:
- 开发了一个数据集,与对齐的孟加拉文本和语音模式.
- 实现了平衡的情感分布和发言人多样性.
- 验证了数据准确性,并为可靠的研究消除了噪音.
结论:
- 该数据集为多式联运情绪分类和情绪感知语音识别提供了宝贵的资源.
- 它允许对低资源语言的多式模式学习方法进行比较.
- 通过支持孟加拉语语言研究,为AI的语言多样性做出贡献.
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