在多模态语音分析中的不确定性建模,跨越精神病频谱
Morteza Rohanian1,2, Roya Hüppi3, Farhad Nooralahzadeh4
1Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland. morteza.rohanian@uzh.ch.
NPJ digital medicine
|January 22, 2026
概括
这项研究引入了一种多式模式模型,用于分析精神病检测的语音模式. 该模型通过评估语音变异性,有效地预测精神病症状,提高诊断准确度.
科学领域:
- 计算精神病学是一种计算精神病学.
- 语音分析 语音分析
- 精神病谱系障碍 精神病谱系障碍 精神病谱系障碍
背景情况:
- 语音是精神病的关键指标,但个人和情境变化限制了其诊断应用.
- 将语音变化作为不确定性的建模对于准确的精神病评估至关重要.
研究的目的:
- 开发一种综合声学和语言语音特征的多式模式,以预测精神病症状严重程度和相关特征.
- 量化语音信号的不确定性,以提高模型在不同环境中的适应性和解释性.
主要方法:
- 开发了一种结合声学和语言语音数据的多式模式模型.
- 包含每个语音模式的不确定性估计,以适应不同的语音质量和任务环境.
- 利用了114名参与者的语音录音 (32名早期精神病患者,82名精神分裂患者) 用德语进行结构化和叙事性任务.
主要成果:
- 该型号获得了高F1得分83%,校准优异 (ECE=0.045),表现出强大的性能.
- 不确定性估计使模型能够适应语音质量和任务上下文,提高准确性.
- 确定了精神病症状的关键语音标志物,包括音调变化,流性障碍和光谱不稳定性.
结论:
- 具有不确定性估计的多式语音分析模型为诊断精神病谱系障碍提供了一个有希望的方法.
- 这种方法通过适应个人的语言模式和上下文因素来提高诊断准确性和解释性.
- 特定的声学和语言语音标记可靠地表明精神病症状,有助于早期检测和评估.
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