一个精心策划的卢干达语和斯瓦希里语语的众包数据集,用于文本到语音合成
Andrew Katumba1, Sulaiman Kagumire2, Joyce Nakatumba-Nabende2
1Department of Electrical and Computer Engineering, Makerere University, Kampala, Uganda.
Data in brief
|August 12, 2025
概括
这项研究为卢干达语和斯瓦希利语提供了高质量的语音数据集,这对于开发文本转化为语音 (TTS) 系统在资源不足的语言中至关重要. 精选的数据确保了低资源的TTS应用程序的更好的性能.
科学领域:
- 计算语言学 计算语言学
- 语音技术 语言技术
- 低资源语言处理 低资源语言处理
背景情况:
- 为卢干达和斯瓦希利等低资源语言开发文本转化语音 (TTS) 系统是具有挑战性的,因为数据稀缺.
- 现有的语音数据集可能缺乏强大的TTS模型培训所需的质量和特定特征.
研究的目的:
- 创建一个精心策划的,高质量的卢干达语和斯瓦希利语的语音数据集,特别是针对女性发言者.
- 在资源不足的语言环境中支持文本转化为语音 (TTS) 技术的进步.
- 为了使这些语言的端到端TTS系统能够进行可重复的研究.
主要方法:
- 数据来源于Mozilla的Common Voice集体,重点是女性发言者的验证发言.
- 多步策划涉及基于声学特征 (语调,音调,节奏) 的手动扬声器选择.
- 声学验证使用集群与音调特征和MFCC,然后进行音频预处理 (消除沉默,无噪声) 和质量过 (WV-MOS分数>=3.5).
主要成果:
- 最终的数据集包括超过19个小时的卢加达语和15个小时的斯瓦希利语录音.
- 每一套语言都有六个不同的女性发言人的录音.
- 所有的音频片段都与准确的文字转录配对.
结论:
- 精心策划的数据集显著提高了卢干达和斯瓦希利语的语音数据的质量和一致性.
- 这种资源对于研究人员开发和评估低资源语言的TTS系统至关重要.
- 该数据集促进了可重现的实验,并促进了语音生成技术的进步.
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