一个新的混合模型,整合了MFCC和声学参数,用于检测语音障碍
Vyom Verma1, Anish Benjwal1, Amit Chhabra1
1Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Sector-26, Chandigarh, India.
Scientific reports
|December 20, 2023
概括
这项研究引入了一种使用Mel频 cepstral系数 (MFCC),基本频率和光谱中心体检测语音障碍的新方法. 该方法准确地识别了语音障碍,改善了诊断和患者的生活质量.
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
- 医疗保健中的人工智能
背景情况:
- 语音障碍显著影响沟通和生活质量.
- 早期发现语音障碍对于有效管理至关重要.
- 目前的检测方法可能缺乏足够的准确性和效率.
研究的目的:
- 开发和验证一种新的,高度准确的语音障碍检测方法.
- 为了整合声学和Mel频 cepstral 系数 (MFCC) 功能,以增强检测.
- 通过先进的机器学习,改进现有的语音障碍检测技术.
主要方法:
- 这是一种混合方法,结合了人工神经网络 (ANN) 和长短期记忆 (LSTM) 模型.
- 在声学属性 (基本频率,光谱中位) 上训练ANN模型.
- 在MFCC属性上训练LSTM模型,并将堆叠的概率输入到XGBoost分类器中进行最终检测.
主要成果:
- 拟议的VDDMFS方法实现了高性能指标.
- 获得了95.67%的精度,95.36%的灵敏度,96.49%的特异性和96.9%的F1得分.
- 与现有的语音障碍检测技术相比,表现出优异的性能.
结论:
- VDDMFS方法为发声障碍检测提供了强大而准确的解决方案.
- 混合机器学习方法有效地利用了各种语音特性.
- 这一进步有可能改善临床诊断和语音健康管理.
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