The Use of Arabic Vowels to Model the Pathological Effect of Influenza Disease by Wavelets

Comput Math Methods Med. 2019 Dec 4;2019:4198462. doi: 10.1155/2019/4198462. eCollection 2019.

Abstract

Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza's pathological effects on the speech signals of the Arabic vowels "A" and "O." For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel "A" and vowel "O," power spectral density (PSD) and spectrogram were illustrated, where the PSD of "A" and "O" was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel "A" were better than those for vowel "O" for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels "A" as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.

MeSH terms

  • Algorithms
  • Area Under Curve
  • Humans
  • Influenza, Human / diagnosis*
  • Influenza, Human / physiopathology*
  • Language*
  • Male
  • Phonation
  • Phonetics
  • ROC Curve
  • Saudi Arabia
  • Signal Processing, Computer-Assisted*
  • Sound Spectrography*
  • Speech
  • Speech Acoustics
  • Support Vector Machine
  • Voice
  • Voice Disorders
  • Voice Quality*
  • Wavelet Analysis*
  • Young Adult