焦虑状态如何影响语音参数:从真实压力场景进行网络分析研究
Qingyi Wang1,2, Feifei Xu1, Xianyang Wang2
1School of Basic Medicine, Air Force Medical University, Xi'an 710032, China.
Brain sciences
|March 28, 2025
概括
状态焦虑可以通过语音分析来检测. 这项研究发现,发声参数 - - 动 - - 始终与焦虑有关,网络分析揭示了高焦虑状态下不同的语音模式.
科学领域:
- 心理学 心理学 心理学
- 语音科学 语言科学
- 计算语言学 计算语言学
背景情况:
- 对于焦虑评估的传统语音分析产生了不一致的结果,因为它专注于孤立的声学特征.
- 网络视角提供了一种新的方法来理解焦虑状态和声音参数之间的复杂关系.
研究的目的:
- 在生态有效的环境中使用网络分析来探索焦虑状态和声乐参数之间的关系.
- 通过检查语音参数相互作用来识别潜在的语音标记,以评估焦虑.
主要方法:
- 一项涉及316名本科生进行的横截面研究,他们执行了一项图片描述任务.
- 语音记录被分析为五个声学参数:动,基本频率 (F0),形式频率 (F1/F2),强度和语音速度.
- 用网络分析来检查这些参数与自我报告的焦虑水平之间的关系.
主要成果:
- 在动和状态焦虑之间发现了强有力的负面关系,而动是与焦虑相关的唯一一致的语言参数.
- 较高的焦虑水平与强度和F1/F2的结合网络有关,与低焦虑状态中的稀疏网络不同.
- 状态焦虑似乎直接影响动和重组语音特征关系.
结论:
- 语音参数网络可以帮助在现实环境中识别焦虑.
- 在高焦虑状态下,负 jitter-焦虑关系和强度-F1 / F2 合表明潜在的基于语言的焦虑标志物.
- 检查语音参数相互作用,特别是动,对于有效的基于语音的焦虑检测至关重要.
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