语音和声音特征在精神分裂症和抑郁症的分类中的相对重要性
Mark Berardi1, Katharina Brosch2,3, Julia-Katharina Pfarr2,3
1Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany. Mark.Berardi@ukbonn.de.
Translational psychiatry
|September 19, 2023
概括
语音分析准确地区分了精神分裂谱系障碍 (SSD) 和主要抑郁障碍 (MDD) 与健康对照 (HC). 声学和语音病理学特征显示了高的分类准确性,突出了语音模式的差异.
科学领域:
- 精神病学是一个精神病学.
- 计算语言学 计算语言学
- 生物医学工程 生物医学工程
背景情况:
- 语音是精神分裂症谱系障碍 (SSD) 和严重抑郁症 (MDD) 的潜在生物标志物.
- 这些条件需要客观和可重复的分类方法.
研究的目的:
- 研究语音声学和语音病理学特征,以对抑郁症,精神分裂症和健康对照进行分类.
- 评估机器学习模型在区分这些群体方面的准确性.
主要方法:
- 收集了60名参与者的240个语音样本 (20个SSD,20个MDD,20个HC).
- 使用二进制分类支持向量机 (SVM) 模型,具有3度多项式内核.
- 分析了语音声学,语音病理特征,以及与症状严重程度的相关性.
主要成果:
- 实现了高分类准确度:0.947 (HC与SSD),0.920 (HC与MDD) 和0.932 (SSD与MDD). 在此过程中,我们获得了高分类准确度:0.947 (HC与SSD).
- 关键特征包括发音协调,每分钟暂停和语音变化.
- 在语音特征和SSD积极症状之间发现了适度的相关性.
结论:
- 语音分析,包括声学和语音病理学特征,提供了一个非常准确的方法来分类SSD和MDD.
- 与精神运动减缓,和平面影响相关的语言特征区分健康对照,SSD和MDD.
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