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Updated: May 23, 2025

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使用深度学习技术进行图像分类驱动的语音障碍检测.

Nasser Ali Aljarallah1, Ashit Kumar Dutta1, Abdul Rahaman Wahab Sait2

  • 1Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, 13713, Saudi Arabia.

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PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用Mel谱图分类的自动语音障碍检测 (SDD) 模型. 这种新的方法实现了99.1%的准确性,为语言障碍提供了高效和可访问的诊断工具.

关键词:
辅助技术是一种辅助技术.深度学习是一种深度学习.功能提取 功能提取图像的分类图像的分类.语言障碍 语音障碍 语言障碍维森变压器的变压器

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 语言病理学 语音病理学

背景情况:

  • 语音障碍显著影响沟通,社会互动,教育和生活质量.
  • 早期和精确的诊断对于成功的干预至关重要,但目前的临床检查是耗时和主观的.
  • 需要自动语音障碍检测 (SDD) 模型来克服手动临床评估的局限性.

研究的目的:

  • 提出基于Mel光谱图像分类的自动语音障碍检测 (SDD) 模型.
  • 使用先进的机器学习技术,准确有效地识别多种语言障碍.
  • 提高语音障碍诊断工具的可访问性和效率.

主要方法:

  • 从语音样本中生成Mel谱图,使用波形变形 (WT) 杂交技术.
  • 为了从Mel光谱图中提取增强的特征,使用了一个LEVIT变压器.
  • 组合学习 (EL) 方法,结合CatBoost,XGBoost和极端随机树,用于分类. 使用量化意识培训 (QAT) 来减少计算资源.

主要成果:

  • 拟议的模型在VOICED和LANNA数据集上实现了99.1%的特殊准确性.
  • 该模型以有限数量的参数 (80万) 证明了效率.
  • 使用沙普利增量解释 (SHAP) 值来确保模型的可解释性.

结论:

  • 开发的自动化SDD模型显著提高了语音障碍分类的准确性和效率.
  • 这种方法为开发可访问和可靠的诊断工具提供了有希望的前景.
  • 未来的研究可以整合多式联络数据,以便在各种语言和方言中更广泛地应用,从而实现实时临床和远程医疗部署.