一种基于MFCC和浅CNN的语音疾病检测方法.
Xiaoping Xie1, Hao Cai2, Can Li2
1The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, China; Shenzhen Research Institute of Hunan University, Shenzhen, China.
Journal of voice : official journal of the Voice Foundation
|October 27, 2023
概括
这项研究引入了一种使用Mel频率塞普斯特姆系数 (MFCC) 参数和卷积神经网络 (CNN) 的新型语音疾病检测方法. 该方法实现了诊断声的高准确性,改进了现有方法.
科学领域:
- 医疗信息学 医疗信息学
- 语音处理 语音处理
- 医疗保健中的人工智能
背景情况:
- 语音疾病的发病率正在上升,需要先进的诊断工具.
- 远程诊断软件代表了医疗保健技术的重大趋势.
- 声的常见原因包括性失声症,声带,声结,声带多.
研究的目的:
- 开发和验证用于临床应用的准确和高效的语音疾病检测方法.
- 为了利用机器学习来自动分类病态语音条件.
主要方法:
- 收集了352名患者的语音样本,与雅医院合作.
- 提取了Mel频率Cepstrum系数 (MFCC) 参数作为语音特征.
- 开发了一个分类模型,将MFCC特征与单层卷积神经网络 (CNN) 集成在一起.
主要成果:
- 在临床数据集上达到92%的最高准确度,超过了之前的研究.
- 使用高级语音功能评估数据库 (AVFAD) 验证了概括能力,达到98%的准确性.
- 在语音疾病检测的准确性和计算效率方面取得了显著的改进.
结论:
- 拟议的基于MFCC和CNN的方法为声部疾病检测提供了高度准确和高效的解决方案.
- 这种方法具有远程诊断和广泛临床应用的巨大潜力.
- 该方法在临床和标准数据集上都表现出强的性能,表明强大的概括能力.
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