In recent years, developing robust systems for automatic detection of pathological speech has attracted increasing interest among researchers and clinicians. This study proposes an end-to-end approach based on wavelet scattering network (WSN) for detection of pathological speech. In the proposed approach, the WSN (which involves no learning) extracts suitable information from the input raw speech signal and this information is then passed through a multi-layer perceptron (MLP) in order to classify the speech signal as either healthy or pathological. The results show that the proposed approach outperformed a convolutional neural network (CNN) based end-to-end system in distinguishing pathological speech from healthy speech. Furthermore, the proposed system achieved comparable performance with a state-of-the-art traditional system based on hand-crafted features for uncompressed speech, but gave better performance than the traditional system for compressed speech of low bit rates.

中文翻译：

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使用小波散射网络的端到端病态语音检测


近年来，开发用于自动检测病理语音的强大系统引起了研究人员和临床医生越来越多的兴趣。本研究提出了一种基于小波散射网络（WSN）的端到端方法来检测病态语音。在所提出的方法中，WSN（不涉及学习）从输入的原始语音信号中提取合适的信息，然后将该信息传递到多层感知器（MLP），以便将语音信号分类为健康或病态。结果表明，所提出的方法在区分病态语音和健康语音方面优于基于卷积神经网络（CNN）的端到端系统。此外，所提出的系统实现了与基于手工制作的未压缩语音特征的最先进的传统系统相当的性能，但比低比特率压缩语音的传统系统提供了更好的性能。