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• W1988557999 abstract "The clinical diagnosis of voice disorders is based on examination of the rapidly moving vocal folds during phonation (f0: 80–300 Hz) with state-of-the-art endoscopic high-speed cameras. Commonly, analysis is performed in a subjective and time-consuming manner via slow-motion video playback and exhibits low inter- and intra-rater reliability. In this study an objective method to overcome this drawback is presented being based on Phonovibrography, a novel image analysis technique. For a collective of 45 normophonic and paralytic voices the laryngeal dynamics were captured by specialized Phonovibrogram features and analyzed with different machine learning algorithms. Classification accuracies reached 93% for 2-class and 73% for 3-class discrimination. The results were validated by subjective expert ratings given the same diagnostic criteria. The automatic Phonovibrogram analysis approach exceeded the experienced raters’ classifications by 9%. The presented method holds a lot of potential for providing reliable vocal fold diagnosis support in the future." @default.
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• W1988557999 date "2010-09-01" @default.
• W1988557999 modified "2023-10-14" @default.
• W1988557999 title "Automatic diagnosis of vocal fold paresis by employing phonovibrogram features and machine learning methods" @default.
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• W1988557999 doi "https://doi.org/10.1016/j.cmpb.2010.01.004" @default.
• W1988557999 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20138386" @default.
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