一个基于Swin变压器的新语音识别框架,用于治疗脱节障碍
Rabbia Mahum1, Ismaila Ganiyu2, Lotfi Hidri2
1Department of Computer Science, University of Engineering and Technology Taxila, Taxila, 47050, Pakistan. rabbia.mahum@uettaxila.edu.pk.
Scientific reports
|June 16, 2025
概括
这项研究介绍了DSR-Swinoid,这是一种新的Swin变压器模型,用于准确检测脱节性关节炎. 改进后的模型达到98.66%的准确性,大大改善了现有的语音障碍识别方法.
科学领域:
- 语音语言病理学 语音语言病理学
- 人工智能的人工智能
- 生物医学信号处理
背景情况:
- 发音障碍是神经系统疾病中常见的一种语言障碍,如帕金森病和中风,需要及时检测.
- 现有的机器学习方法用于检测脱节性关节炎,由于语音变化和背景噪声,其错误阳性率很高.
研究的目的:
- 开发一个改进的基于Swin变压器的模型,DSR-Swinoid,以更准确地检测失声症的语音.
- 增强Swin变压器的局部特征提取能力,以更好地识别语音障碍.
主要方法:
- 语音信号被转换成Mel谱图以捕获语音模式.
- 开发了一个新的DSR-Swinoid模型,集成了四个模块 (NLF,卷积补丁连接,MP,MVB) 来增强Swin变压器中的本地特征学习.
主要成果:
- DSR-Swinoid模型在检测缺动性关节炎方面取得了98.66%的高精度.
- 与现有方法相比,拟议的模型表现出优越的性能.
结论:
- DSR-Swinoid模型在精确检测脱节性关节炎方面取得了重大进展.
- 增强的Swin变压器架构有效地解决了以前方法的局限性,为改进的临床诊断铺平了道路.
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