声学特征控制正常,良性和恶性声音的变化
Boquan Liu1, Jianhan Lei1, Owen P Wischhoff2
1School of Humanities, Shanghai Jiao Tong University, Shanghai, China.
概括
主要组件分析 (PCA) 确定了声病变 (VFL) 的独特声学特征. 较高的形成频率区分恶性VFL,而音调变化表明良性VFL,有助于诊断.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 声学分析 声学分析
- 语音病理学 语音病理学
背景情况:
- 声病变 (VFLs) 需要准确的诊断,因为良性和恶性类型的治疗方法不同.
- 多学科的方法对于识别可疑的VFL至关重要.
- 这项研究研究了声学特征,以区分良性和恶性VFL.
研究的目的:
- 确定特定的声学特征,区分良性与恶性声病变 (VFLs).
- 使用主要组件分析 (PCA) 来识别关键的声学差异.
- 探索各种良性VFL之间的声学差异.
主要方法:
- 对157名参与者进行了声学语音分析 (正常,良性VFL,恶性VFL).
- 主要组件分析 (PCA) 用于分析声学变量.
- 2020年5月至2021年7月,在一家专门医院采集了语音样本.
主要成果:
- 恶性VFLs显示出更高的形成频率和变异性.
- 良性VFL显示出显著的音调 (F0) 变化.
- 特定的声学标记物被确定为雷因克的 (形成物分散),多 (亚和比),囊 (F0和形成物分散) 和白血病 (形成物频率).
结论:
- 主要组件分析 (PCA) 有效地识别了由VFLs引起的声音声学中的振动变化.
- 截然不同的声学特征区分了各种良性和恶性声状况.
- 这些发现支持开发用于早期恶性VFL检测和管理的自动化算法.
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