综合声调偏差指数 (IVDI):一种机器学习模型,用于对声调偏差的一般级别进行分类
Luiz Medeiros Araujo Lima-Filho1, Leonardo Wanderley Lopes2, Telmo de Menezes E Silva Filho3
1Department of Statistics, Member of the Graduate Program in Decision Models and Health, Universidade Federal da Paraíba - UFPB, João Pessoa, Paraíba, Brazil.
Journal of voice : official journal of the Voice Foundation
|November 26, 2024
概括
一个新的机器学习指数准确地使用声学和听觉感知措施来分类声音偏差. 这个综合语音偏差指数在识别语音障碍方面表现出色.
科学领域:
- 语音和听力科学 语言和听力科学
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
背景情况:
- 语音偏差 (GG) 评估对于诊断语音障碍至关重要.
- 目前的方法依赖于主观的听觉感知判断 (APJ) 和客观的声学分析.
- 需要采用统一的,数据驱动的方法来提高分类准确性.
研究的目的:
- 使用机器学习 (ML) 开发一个多参数指数,以预测和分类声调偏差 (GG) 的整体程度.
- 将声学测量和APJ整合到一个强大的预测模型中.
- 为了验证模型在分类语音偏差严重性的表现.
主要方法:
- 利用了300名参与者的数据 (异声和非异声).
- 收集了持续的元音和连接的语音样本.
- 提取了47个声学特征,并纳入了APJ等级 (GR,GB,GI,GS) 用于ML模型开发.
- 采用梯度提升用于分类和特征的重要性变量选择.
主要成果:
- 梯度提升模型实现了出色的性能.
- 选择的特征包括四个声学测量 (jitterLoc,光滑的石峰突出,HNRmean,相关性) 和四个APJ测量 (GR,GB,GS,GI).
- 最终的模型实现了93.75%的分类准确度和0.9374.4的加权卡帕.
结论:
- 综合声调偏差指数,结合声学和APJ措施,表现出色.
- 这个指数有效地根据声音偏差的总体程度对声音进行分类.
- 基于ML的方法为客观的语音障碍评估提供了一个有希望的工具.
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