DocumentCode :
3747938
Title :
Dimensionality reduction for voice disorders identification system based on Mel Frequency Cepstral Coefficients and Support Vector Machine
Author :
Nawel Souissi;Adnane Cherif
Author_Institution :
Faculty of Sciences of Tunis, University of Tunis El-Manar, Innov´COM Laboratory, 2092, Tunis, Tunisia
fYear :
2015
Firstpage :
1
Lastpage :
6
Abstract :
Nowadays, due to the severe daily activities and vocal abuse, many diseases affect the mechanism of voice production which causes pathological voices. Therefore, the identification of voice diseases becomes a real challenge. In this context, the automatic speech recognition can provide great results as a complementary tool to other medical techniques. This paper proposes a reliable algorithm based on short-term cepstral parameters, Linear Discriminant Analysis (LDA) as dimensionality reduction method and Support Vector Machine (SVM) as classifier. A full comparative study is established and the system performance is evaluated in terms of accuracy, sensitivity, specificity, precision and Area Under Curve (AUC). Our findings demonstrate that the detection of voice disorders can be efficient using only the original Mel Frequency Cepstral Coefficients (MFCC) ignoring their first and second derivative.
Keywords :
"Mel frequency cepstral coefficient","Support vector machines","Pathology","Classification algorithms","Sensitivity","Speech"
Publisher :
ieee
Conference_Titel :
Modelling, Identification and Control (ICMIC), 2015 7th International Conference on
Type :
conf
DOI :
10.1109/ICMIC.2015.7409479
Filename :
7409479
Link To Document :
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