波形分析和对一致特征提取在语音病理的分类中的应用.
Gabriel José Pellisser Dalalana1, Rodrigo Capobianco Guido2, Eduardo Sperle Honorato3
1Department of Electrical and Computer Engineering, School of Engineering of São Carlos, University of São Paulo, São Paulo, Brazil.
Journal of voice : official journal of the Voice Foundation
|February 22, 2025
概括
这项研究引入了一种新的方法,使用波纹分析和对一致逻辑来准确地分类语音病理学. 该方法有效地区分健康和失调的声音,提供一个计算高效的诊断工具.
科学领域:
- 信号处理 信号处理
- 计算智能是一种计算智能.
- 语音科学 语言科学
背景情况:
- 准确地分类语音病理对于诊断和治疗至关重要.
- 传统方法可能会与固有的不确定性作斗争,并且在语音信号数据中重叠.
研究的目的:
- 开发和评估用于语音病理学分类的组合波纹分析和对一致逻辑框架.
- 确定最佳的波形波器和特征,以区分健康和病态的语音样本.
主要方法:
- 语音信号被使用离散时间波纹数据包转换分解.
- 像能量和零穿越率 (ZCR) 这样的特征被提取并使用支向量机进行分类.
- 为了管理分类不确定性,采用了对一致的逻辑框架.
主要成果:
- 拟议的方法实现了高分类准确性,超过了现有的最先进的方法.
- 特定的波形波器 (如Sym32,Daub4,Haar) 和特征 (能量,ZCR) 显示出不同病理的优异性能.
- 超一致逻辑有效地处理了类重叠和不确定性.
结论:
- 波形分析与对一致逻辑相结合,为语音病理学分类提供了强大的和高效的方法.
- 这种综合方法对临床应用和实时诊断工具的开发充满希望.
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