噪音对深度学习的影响,用于对病态语音的分类
Koki Hasebe1, Shintaro Fujimura1, Tsuyoshi Kojima1
1Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
The Laryngoscope
|January 27, 2024
概括
背景噪音对使用GRBAS尺度评估语音障碍的机器学习模型产生重大影响. 性能指标如精度和F1得分随着噪声强度的增加而大幅下降,突出了对抗噪声模型的需求.
科学领域:
- 计算语言学 计算语言学
- 医学声学 医学声学
- 人工智能在医学中的应用
背景情况:
- 机器学习模型越来越多地用于语音障碍评估.
- GRBAS尺度是评估语音质量的标准工具.
- 背景噪音可能会影响自动语音分析系统的性能.
研究的目的:
- 为了评估背景噪声对机器学习模型的影响,评估语音障碍GRBAS尺度.
- 为了确定1D卷积神经网络 (CNN) 的噪声弹性,用于语音分析.
主要方法:
- 1406个语音样本的数据集被用来训练一个5层1D CNN.
- 将高斯噪声添加到不同强度的测试样本中.
- 用准确度,F1分数和科恩的卡帕来评估模型的性能.
主要成果:
- 在GRBAS尺度上的模型性能随着噪声强度的增加而显著下降.
- 对于G尺度,准确度从70.9%降至8.5%,F1得分从69.2%降至1.3%,Cohen的卡帕从0.679降至0.0.0.
- 对于其他GRBAS组件 (R,B,A,S) 也观察到类似的性能下降.
结论:
- 背景噪音大大降低了GRBAS规模评估的机器学习模型的性能.
- 需要耐噪技术,例如数据增强,以提高现实应用中的模型稳定性.
- 建议进行进一步的研究,以开发适应噪音的AI用于语音障碍诊断.
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