机器学习压力下的语音生成:79个案例和8个信号的多式数据集
Jan Pešán1, Vojtěch Juřík2,3, Alexandra Ružičková4
1Speech@FIT, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
Scientific data
|November 12, 2024
概括
使用语音分析早期检测压力对于安全关键的工作至关重要. 这项研究引入了一套新的数据集,以改进自动压力估计的 paralinguistic语音特征.
科学领域:
- 认知科学 认知科学
- 语音处理 语音处理
- 人与计算机的交互
背景情况:
- 高风险职业 (飞行员,外科医生) 的认知和身体过度负担会影响决策,并构成安全风险.
- 双语语音分析为早期压力检测提供了一个有前途的途径,捕捉了字面转录之外的细微差别.
- 现有的研究面临着数据瓶,缺乏高质量的参考数据集来训练人工压力检测系统.
研究的目的:
- 为了解决数据稀缺的问题,用于压力检测的 paralinguistic 语音分析.
- 通过从语音中准确估计压力,促进预防性干预系统的发展.
- 为推进自动压力检测研究提供原始的实证数据集.
主要方法:
- 使用BESST实验协议收集了一个新的经验数据集.
- 专注于在诱导的压力条件下捕获语音信号.
- 运用 paralinguistic 语音特征 (强度,语调,节奏) 进行分析.
主要成果:
- 成功收集了一个新的语音信号在诱导压力下的数据集.
- 该数据集旨在克服培训平行语言分析系统的数据瓶.
- 促进了从演讲到现实世界的应用对压力估计的研究.
结论:
- 这一数据集对于训练人工系统来检测语音压力至关重要.
- 这一资源将加速先发性干预系统的发展.
- 增强的双语语音处理是提高关键决策角色安全性的关键.
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