Automatic Classification of Neurological Voice Disorders Using Wavelet Scattering Features

Madhu Yagnavajjula*, Kiran Mittapalle, Paavo Alku, Krothapalli Sreenivasa Rao, Pabitra Mitra

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
9 Downloads (Pure)

Abstract

Neurological voice disorders are caused by problems in the nervous system as it interacts with the larynx. In this paper, we propose to use wavelet scattering transform (WST)-based features in automatic classification of neurological voice disorders. As a part of WST, a speech signal is processed in stages with each stage consisting of three operations – convolution, modulus and averaging – to generate low-variance data representations that preserve discriminability across classes while minimizing differences within a class. The proposed WST-based features were extracted from speech signals of patients suffering from either spasmodic dysphonia (SD) or recurrent laryngeal nerve palsy (RLNP) and from speech signals of healthy speakers of the Saarbruecken voice disorder (SVD) database. Two machine learning algorithms (support vector machine (SVM) and feed forward neural network (NN)) were trained separately using the WST-based features, to perform two binary classification tasks (healthy vs. SD and healthy vs. RLNP) and one multi-class classification task (healthy vs. SD vs. RLNP). The results show that WST-based features outperformed state-of-the-art features in all three tasks. Furthermore, the best overall classification performance was achieved by the NN classifier trained using WST-based features.

Original languageEnglish
Article number103040
Number of pages10
JournalSpeech Communication
Volume157
DOIs
Publication statusPublished - Feb 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • MFCC
  • neural network
  • neurological voice disorder
  • spasmodic dysphonia
  • support vector machine
  • wavelet scattering transform
  • Spasmodic dysphonia
  • Neurological voice disorder
  • Neural network
  • Support vector machine
  • Wavelet scattering transform

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