Automatic Wheezing Detection Using Speech Recognition Technique

Bor Shing Lin, Bor-Shyh Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


This study developed a speech recognition technique to detect wheezing. Wheezes are important in the diagnosis of pulmonary pathologies such as asthma. The acoustic features of wheezes are distinct in the frequency domain. Therefore, many studies have focused on detecting wheezing peaks in spectrograms through image processing. However, automated detection of wheezing peaks is difficult because of blurred edges and noise. This paper proposes an alternative approach for wheezing detection in which the mel frequency cepstral coefficients (MFCCs) are integrated into the Gaussian mixture model (GMM). The MFCCs reduce the short-term spectral information to a few coefficients, and the GMM recognizes the respiratory sounds. The respiratory sounds of 18 volunteers (9 asthmatic and 9 normal adults) were recorded for training and testing. The results of a qualitative analysis of wheeze recognition showed a good sensitivity of 0.881 and a high specificity of 0.995.

Original languageEnglish
Pages (from-to)545-554
Number of pages10
JournalJournal of Medical and Biological Engineering
Issue number4
StatePublished - 1 Aug 2016


  • Asthma
  • Gaussian mixture model
  • Mel frequency cepstral coefficients
  • Short-term spectra
  • Wheezing


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