The acoustic-prosodic qualities of a speech signal are influenced by various health-related factors, owing to their complex and intricate nature. In the speech and health domain, machine learning research is active and expanding, with a focus on devising paradigms to objectively extract and measure such effects. The field of biomedical engineering has great promise in the development of non-invasive diagnostic procedures utilizing voice as a means of assessment. The utilization of speech signals for the purpose of screening for the upper respiratory tract infections (URTI) such as common cold may offer potential advantages in terms of mitigating its transmission. In this study, we have proposed a novel time-frequency domain deep convolutional neural network for URTI detection from speech using the Chirplet transform. The time-frequency representation of speech signal is achieved using the Chirplet transform. Then, a deep convolutional neural network is used for the classification of the time-frequency representation of healthy and URTI speech signals. The effectiveness of the proposed approach is assessed through the utilization of the URTIC database. We have achieved the UAR of 68.97% and 67.34% on the develop and test set of the URTIC database, respectively.

中文翻译：

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“你听起来不太好，你应该休息一天”：使用时频域深度卷积神经网络从语音中自动检测上呼吸道感染

由于其复杂性和复杂性，语音信号的声学韵律质量受到各种与健康相关的因素的影响。在语音和健康领域，机器学习研究非常活跃且不断扩展，重点是设计范式来客观地提取和衡量此类影响。生物医学工程领域在开发利用语音作为评估手段的非侵入性诊断程序方面具有巨大的前景。利用语音信号来筛查普通感冒等上呼吸道感染 (URTI) 可能在减轻其传播方面具有潜在优势。在这项研究中，我们提出了一种新颖的时频域深度卷积神经网络，用于使用 Chirplet 变换从语音中检测 URTI。语音信号的时频表示是使用Chirplet 变换实现的。然后，使用深度卷积神经网络对健康语音信号和 URTI 语音信号的时频表示进行分类。通过使用 URTIC 数据库来评估所提出方法的有效性。我们在 URTIC 数据库的开发集和测试集上分别实现了 68.97% 和 67.34% 的 UAR。