Telephony-based voice pathology assessment using automated speech analysis. Export

@article{moran:06, 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.}, address = {Department of Electronic and Electrical Engineering, University College Dublin, Dublin 4, Ireland. rosalyn.moran@ee.ucd.ie}, author = {Moran, R. J. and Reilly, R. B. and de Chazal, P. and Lacy, P. D.}, citeulike-article-id = {2847073}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TBME.2005.869776}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16532773}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16532773}, doi = {10.1109/TBME.2005.869776}, issn = {0018-9294}, journal = {IEEE transactions on bio-medical engineering}, keywords = {features, pathology, speech}, month = {March}, number = {3}, pages = {468--477}, posted-at = {2008-05-30 12:04:11}, priority = {2}, title = {Telephony-based voice pathology assessment using automated speech analysis.}, url = {http://dx.doi.org/10.1109/TBME.2005.869776}, volume = {53}, year = {2006} }

TY - JOUR ID - moran:06 L3 - citeulike-article-id:2847073 N2 - 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. IS - 3 JF - IEEE transactions on bio-medical engineering SN - 0018-9294 AD - Department of Electronic and Electrical Engineering, University College Dublin, Dublin 4, Ireland. rosalyn.moran@ee.ucd.ie EP - 477 TI - Telephony-based voice pathology assessment using automated speech analysis. VL - 53 SP - 468 KW - features KW - pathology KW - speech AU - Moran, RJ AU - Reilly, RB AU - de Chazal, P AU - Lacy, PD PY - 2006/03// UR - http://dx.doi.org/10.1109/TBME.2005.869776 ER -