End-to-End Pathological Speech Detection Using Wavelet Scattering Network

• Published in: IEEE signal processing letters Vol. 29; pp. 1863 - 1867
• Main Authors: Reddy, Mittapalle Kiran, Keerthana, Yagnavajjula Madhu, Alku, Paavo
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; CNN; Convolutional neural networks; Feature extraction; MFCC; MP3 compression; Multilayer perceptrons; Multilayers; openSMILE features; pathological speech; Pathology; Scattering; Signal classification; Speech compression; Task analysis; Wavelet scattering network; Wavelet transforms; Wireless sensor networks
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2022.3199669

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.