Abstract
Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology. To do so, utterances of the sustained vowel ah are employed from the Massachusetts Eye and Ear Infirmary database of disordered speech. Linear prediction coefficients extracted from the aforementioned utterances are used as features. The receiver operating characteristic curve of the linear classifier, that stems from the Bayes classifier when Gaussian class conditional probability density functions with equal covariance matrices are assumed, is derived. The optimal operating point of the linear classifier is specified with and without reject option. First results using utterances of the rainbow passage are also reported for completeness. The reject option is shown to yield statistically significant improvements in the accuracy of detecting the voice pathologies under study.
Highlights
Vocal pathologies arise due to accident, disease, misuse of the voice, or surgery affecting the vocal folds and have a profound impact on patients’ life
Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology
Sustained vowels are not representative of continuous speech, utterances of the sustained vowel “ah” from the Massachusetts Eye & Ear Infirmary (MEEI) database are employed here due to their wide use in medical practice and, primarily, in order to maintain direct compatibility with previously reported results [29, 32] and minimal problem complexity, so that we focus on the role of the reject option
Summary
Vocal pathologies arise due to accident, disease, misuse of the voice, or surgery affecting the vocal folds and have a profound impact on patients’ life. Several techniques for the detection and classification of voice pathologies by means of acoustic analysis, parametric and non-parametric feature extraction, and pattern recognition are reviewed in [21]. In all these techniques, first, descriptive features are extracted from the speech signal. Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology.
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