决策树与线性支向量机分类器在中介语音声音的选中:寻求一个合理的理由.
Emilian Erman Mahmut1, Stelian Nicola1, Vasile Stoicu-Tivadar1
1Politehnica University Timisoara, Romania Department of Automation and Applied Informatics.
Studies in health technology and informatics
|October 23, 2023
概括
这项研究使用两个分类器增强了语音声音障碍 (SSD) 查算法. 决策树分类器实现了100%的准确性,超过了线性支向量机 (SVM) 模型.
科学领域:
- 计算语言学 计算语言学
- 语言病理学 语音病理学
- 机器学习 机器学习
背景情况:
- 语音声音障碍 (SSD) 影响儿童的发育.
- 准确的查对于早期干预至关重要.
- 现有的算法需要改进以改善分类.
研究的目的:
- 为了评估两个机器学习分类器对SSD选的性能.
- 将分类和回归树 (CART) 模型与线性支向量机器 (SVM) 进行比较.
- 为了确定SSD选算法的最佳分类器.
主要方法:
- 提取了10个音频特征,用于中间语音声音.
- 整合了一个语音语言病理学家 (SLP) 验证功能.
- 在语音样本数据上训练并测试CART和线性SVM模型.
主要成果:
- 决策树分类器实现了100%的准确性.
- 线性SVM分类器实现了98.2%的准确性.
- 这两种模型在30%的测试数据子集上都表现出高性能.
结论:
- 决策树分类器显示了SSD选的卓越性能.
- 开发的算法显示了准确和高效的SSD识别的希望.
- 需要进一步的研究来完善算法并探索其临床应用.
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