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一个数据集用于将短语和句子根据音调调分类为陈述,问题或呼喊
Ayub Othman Abdulrahman1, Shanga Ismail Othman1, Gazo Badran Yasin1
1Department of Computer Science, College of Science, University of Halabja, Kurdistan Region, F.R., Halabja, Iraq.
本研究介绍了SQEBSP数据集,其中包括12,660个库尔德语语音录音以基于音高的分类. 此资源有助于开发低资源语言的语音技术.
科学领域:
- 语言学的语言学.
- 计算机科学 计算机科学
- 语音技术 语言技术
背景情况:
- 自然语言处理 (NLP) 的进步增强了人与计算机的互动.
- 深度学习提高了语音识别,专注于音调等声学线索.
- 音调的变化对prosodic分类至关重要,特别是在音调,像库尔德语这样的低资源语言中.
研究的目的:
- 介绍基于音调 (SQEBSP) 数据集的陈述,问题或呼喊.
- 为开发和测试基于音调的语音分类算法提供资源.
- 支持发音建模和方言敏感语音技术的研究,用于资源不足的语言.
主要方法:
- 收集了来自431名库尔德语母语使用者的12660段专业录制的语音片段.
- 每位演讲者在三大类别中表达了10个新短语:陈述,问题和惊呼.
- 数字化16kHz的发言和手动验证的基于音调的分类正确性.
主要成果:
- SQEBSP数据集提供了均衡的表现,每个类大约有4200个样本 (陈述,问题,惊呼).
- 包括元数据,如说话者的性别,年龄组和句子标识符.
- 提供原始的音频文件,Mel-Frequency Cepstral Coefficients (MFCCs) 和波形可视化.
结论:
- SQEBSP数据集对于培训和评估基于音调的语音分类模型是有价值的.
- 促进了对数据有限的语言的发音建模的进步.
- 帮助开发对语言方言敏感的语音技术.
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