在患有帕金森病的患者中,发言失语的声学模型
1Department of Clinical Science, Faculty of Medicine, Umeå University, Umeå, Västerbotten, Sweden.
Frontiers in human neuroscience
|May 13, 2025
概括
自动声学分析可以接近人类专家对帕金森病 (PD) 失灵症的评级. 关键预测因素包括在语调转折点上的音调变化,而不仅仅是整体音调变化.
科学领域:
- 神经学 神经学
- 语音语言病理学 语言病理学
- 计算语言学 计算语言学
背景情况:
- 帕金森病 (PD) 常常会导致发音障碍,影响语音节奏和语调.
- 准确评估dysprosody严重程度对于患者管理至关重要,但依赖于主观的专家评估.
- 自动声学分析提供了一个潜在的客观和可扩展的方法来评估dysprosody.
研究的目的:
- 为了确定声学特征,最好预测帕金森病患者的失调症严重程度.
- 开发和验证一个用于dysprosody的自动声学评估程序.
- 为了比较机器学习模型在预测专家异常评级方面的表现.
主要方法:
- 分析了68名PD患者的108个语音录音 (含有和没有levodopa).
- 四位临床专家评估了感知障碍症的严重程度.
- 一个语音处理管道提取了声学特征,并训练了五个机器学习模型并进行了交叉验证.
主要成果:
- 最重要的声学预测指标是基本频率 (f0) 变化的变化和第一个Mel-frequency cepstral系数在音调转折点上的变化.
- 发言范围f0中的变量是一个不如预期的不那么重要的预测因素.
- 机器学习模型展示了接近人类专家评估的能力.
结论:
- 自动声学评估可以可靠地近似人类专家在PD.
- 这种程序在缺乏专家可用性的临床环境中可能是有价值的.
- 单单是音调的变化不足以完全描述PD相关的失调症.
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