论文标题
环境噪音对阿尔茨海默氏病检测的影响：您应该让婴儿哭泣吗？
Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?
论文作者
论文摘要
鉴于AD的高流行和传统方法的高成本，与自动检测阿尔茨海默氏病（AD）有关的研究很重要。由于AD显着影响自发语音的声学，因此语音处理和机器学习（ML）为可靠地检测AD提供了有希望的技术。但是，语音音频可能会受到不同类型的背景噪声的影响，重要的是要了解噪声如何影响ML模型从语音中检测AD的准确性。在本文中，我们研究了来自五个不同类别的15种类型的环境噪声的影响，对训练有三种类型的声学表示的四种ML模型的性能。我们进行了彻底的分析，以显示ML模型和声学特征如何受到不同类型的声学噪声的影响。我们表明，声学噪声不一定是有害的 - 某些类型的噪声对AD检测模型有益，并帮助将精度提高到4.8％。我们提供有关如何利用声学噪声的建议，以通过在现实世界中部署的ML模型获得最佳性能结果。
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the acoustics of spontaneous speech, speech processing and machine learning (ML) provide promising techniques for reliably detecting AD. However, speech audio may be affected by different types of background noise and it is important to understand how the noise influences the accuracy of ML models detecting AD from speech. In this paper, we study the effect of fifteen types of environmental noise from five different categories on the performance of four ML models trained with three types of acoustic representations. We perform a thorough analysis showing how ML models and acoustic features are affected by different types of acoustic noise. We show that acoustic noise is not necessarily harmful - certain types of noise are beneficial for AD detection models and help increasing accuracy by up to 4.8\%. We provide recommendations on how to utilize acoustic noise in order to achieve the best performance results with the ML models deployed in real world.
