End-to-end Pathological Speech Detection using Wavelet Scattering Network

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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.
Original languageEnglish
Pages (from-to)1863-1867
Number of pages5
JournalIEEE Signal Processing Letters
Publication statusPublished - 17 Aug 2022
MoE publication typeA1 Journal article-refereed


  • Wavelet scattering network
  • CNN
  • pathological speech
  • MFCC
  • openSMILE features
  • MP3 compression


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