使用Wav2vec 2.0特征提取的语音障碍分类
Jie Cai1, Yuliang Song2, Jianghao Wu1
1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Journal of voice : official journal of the Voice Foundation
|September 26, 2024
概括
这项研究有效地使用 wav2vec 2.0 对特征提取和机器学习模型进行正常和病态声音的分类. 随机森林模型在语音分析中表现出卓越的准确性和稳定性.
科学领域:
- 语音处理和生物医学信号分析.
- 深度学习和机器学习在医疗保健中的应用.
背景情况:
- 准确的语音分类对于诊断各种病理状况至关重要.
- 传统方法可能缺乏复杂语音模式所需的特征提取能力.
研究的目的:
- 用 wav2vec 2.0 来进行特征提取来分类正常与病态声音.
- 评估不同机器学习分类器在此任务中的表现.
主要方法:
- 利用 wav2vec 2.0 模型从语音录音中提取特征.
- 训练并评估了支持向量机 (SVM),K-最近邻居,决策树 (DT) 和随机森林 (RF) 模型.
- 采用分层K折交叉验证和各种性能指标,包括准确性,精度,回忆,F1得分,ROC曲线和混矩阵.
主要成果:
- 随机森林 (RF) 模型实现了最高的精度 (0.98 ± 0.02) 和其他指标的强性能.
- 决策树 (DT) 模型显示出极好的精度和平衡的性能.
- 数据增强显著提高了所有模型的性能,特别是RF和DT.
结论:
- wav2vec 2.0和机器学习模型的结合对语音分类非常有效.
- 在区分正常声音和病态声音方面取得了卓越的准确性和稳定性.
- 射频和DT模型特别适合于这种语音分类任务.
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