多任务和转移学习方法用于联合分类和音障的严重程度估计
1Department of Telecommunications and Media InformaticsBudapest University of Technology and Economics 1117 Budapest Hungary.
IEEE journal of translational engineering in health and medicine
|January 10, 2024
概括
多任务学习 (MTL) 通过使用共享功能来改善语音障碍的分类和严重程度估计. 这种方法提高了语音病理学家的临床理解和患者监测.
科学领域:
- 语音分析 语音分析
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 语音分析提供了关于健康,情绪和身份的见解.
- 语音障碍会影响声乐器官,导致沟通困难.
- 以前的研究通常集中在单个语音分析任务上.
研究的目的:
- 开发一种多任务学习 (MTL) 方法,用于同时对失声症进行分类和严重程度估计.
- 利用共享表示和计算机视觉模型来改进语音分析.
- 调查辅助任务,如说话者的性别分类,对表现的影响.
主要方法:
- 语音分析的综合转移学习和多任务学习 (MTL).
- 调整了五个计算机视觉模型以支持MTL.
- 进行了二元和多类失声症分类,以发言者的性别作为辅助任务.
主要成果:
- 与单任务学习 (STL) 相比,MTL在各个分类指标上表现优越.
- 获得了F1分数93% (MTL) 和90% (STL) 的分类.
- 与STL的73.2%相比,MTL的准确性达到77%,严重程度估计可比.
结论:
- MTL提高了语音障碍分类和严重程度估计的准确性.
- 拟议的方法有助于临床医生了解患者的病情和监测进展.
- 整合分类和严重程度估计可以改善声质监测和治疗评估.
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