基于融合特征MSA和1D的中风后肌痛声识别
Ye Wujian1, Zheng Yingcong1, Chen Yuehai1
1School of Integrated Circuit, Guangdong University of Technology, Guangzhou, Guangdong, China.
Computer methods in biomechanics and biomedical engineering
|October 18, 2024
概括
一个新的深度学习模型有效地使用新的融合功能和改进的 1D ResNet 混合双向 LSTM 网络来识别中风后肌痛 (PSD). 这种方法增强了病态语音识别,以便更好地诊断.
科学领域:
- 神经学 神经学
- 语言病理学 语音病理学
- 人工智能的人工智能
背景情况:
- 脑卒中后性肌痛症 (PSD) 是一种常见的,可能危及生命的中风并发症,影响患者的生活质量.
- 目前用于识别病态声音的方法通常依赖于频域特征,这些特征可能无法完全捕捉声音特征.
- 现有的方法在PSD评估中的实际应用有局限性.
研究的目的:
- 提出一个改进的深度学习模型来分类病态和正常的声音在中风后失联症.
- 引入一种新型的融合功能 (MSA),用于增强病态语音识别.
- 开发一个混合的1D ResNet网络,具有扩展卷积和双向LSTM (1D DRN-biLSTM).
主要方法:
- 利用了一种新的融合特征 (MSA),结合了多个语音特征.
- 实现了一个改进的1D ResNet网络混合双向LSTM与扩展卷积 (1D DRN-biLSTM).
- 在音节和扬声器层面分类病态和正常的声音.
主要成果:
- 与单独的MFCC特征相比,拟的融合特征显著改善了病态语音识别.
- 1D DRN-biLSTM模型由于整合了扩展卷积和LSTM,表现出了卓越的性能.
- 实现了高准确率:82.41%在音节级别和100%在扬声器级别.
结论:
- 开发的深度学习模型具有新的融合功能,为PSD提供了改进的功能学习和识别率.
- 这种方法显示出有很大的潜力,可以提高评估和诊断中风后肌痛.
- 该方法的性能优于现有方法,为在中国更有效的临床应用铺平了道路.
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