Telephony-based voice pathology assessment using automated speech analysis

Rosalyn J Moran, Richard B Reilly, Philip de Chazal, Peter D Lacy

Research output: Contribution to journalArticle (Academic Journal)peer-review

112 Citations (Scopus)

Abstract

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.

Original languageEnglish
Pages (from-to)468-77
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume53
Issue number3
DOIs
Publication statusPublished - Mar 2006

Keywords

  • Algorithms
  • Artificial Intelligence
  • Diagnosis, Computer-Assisted
  • Humans
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sound Spectrography
  • Speech Disorders
  • Speech Production Measurement
  • Speech Recognition Software
  • Telemedicine
  • Telephone

Fingerprint

Dive into the research topics of 'Telephony-based voice pathology assessment using automated speech analysis'. Together they form a unique fingerprint.

Cite this