Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Automatic detection of pathologies in the voice by HOS based parameters
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Interactions between speech coders and disordered speech
Speech Communication
Fundamental frequency estimation of voice of patients with laryngeal disorders
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Spoken language analysis, modeling and recognition-statistical and adaptive connectionist approaches
On optimal reject rules and ROC curves
Pattern Recognition Letters
Principal Component Analysis of Spectral Perturbation Parameters for Voice Pathology Detection
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
EURASIP Journal on Applied Signal Processing
Pathological Voice Classification Based on a Single Vowel's Acoustic Features
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Detection of vocal fold paralysis and edema using linear discriminant classifiers
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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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.