人类声音传达值得信赖的意图:人口统计学上多样化的语音音频数据集
Constantina Maltezou-Papastylianou1,2, Reinhold Scherer3, Silke Paulmann4
1Department of Psychology and Centre for Brain Science, University of Essex, Colchester, CO4 3SQ, UK. cm19066@essex.ac.uk.
Scientific data
|May 31, 2025
概括
一个新的,开放式访问的语音音频数据集具有不同的人口统计学特征,以改善语音感知研究. 本资源有助于开发更普遍的模型来识别各种人群中值得信赖的言论.
科学领域:
- 语音感知 语音感知
- 声学语音学的声音学
- 机器学习 机器学习
背景情况:
- 现有的语音音频数据集缺乏年龄,种族和性别的多样性,限制了研究的概括性.
- 目前的数据集主要包括年轻的白人成年演讲者,这阻碍了语音感知和可信度研究的广泛应用.
研究的目的:
- 介绍一个新的,开放式访问的语音音频数据集与多样化的人口代表.
- 为推进声音感知研究和可信言语的分类提供资源.
- 促进开发更具包容性和准确性的语音分析模型.
主要方法:
- 收集了来自96个未经训练的白人,黑人和南亚背景的96个未经训练的演讲者的1,152个发言.
- 包括年轻人 (18-45岁) 和老年人 (60岁以上) 的年龄组,记录中立和故意值得信赖的语言.
- 使用线性和非线性分类模型分析了声学和语音质量特征.
主要成果:
- 在使用新数据集对中立和可信言论进行分类时,获得了大约70%的准确性.
- 证明了数据集在区分语音意图方面的实用性.
- 突出了使用更多样化的数据改善模型性能的潜力.
结论:
- 开发的数据集解决了可访问,多样化的语音音频资源的关键差距.
- 这一开放式数据集将促进对不同人口群体的语音感知和可信度的进一步研究.
- 有助于提高语音分析技术的通用性和现实世界的适用性.
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