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 language | English |
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Pages (from-to) | 468-77 |
Number of pages | 10 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 53 |
Issue number | 3 |
DOIs | |
Publication status | Published - 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