Artificial Neural Networks and Support Vector Machine for Voice Disorders Identification
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 5
Abstract
The diagnosis of voice diseases through the invasive medical techniques is an efficient way but it is often uncomfortable for patients, therefore, the automatic speech recognition methods have attracted more and more interest recent years and have known a real success in the identification of voice impairments. In this context, this paper proposes a reliable algorithm for voice disorders identification based on two classification algorithms; the Artificial Neural Networks (ANN) and the Support Vector Machine (SVM). The feature extraction task is performed by the Mel Frequency Cepstral Coefficients (MFCC) and their first and second derivatives. In addition, the Linear Discriminant Analysis (LDA) is proposed as feature selection procedure in order to enhance the discriminative ability of the algorithm and minimize its complexity. The proposed voice disorders identification system is evaluated based on a widespread performance measures such as the accuracy, sensitivity, specificity, precision and Area Under Curve (AUC).
Authors and Affiliations
Nawel SOUISSI, Adnane CHERIF
Keywords
Related Articles
- EP ID EP133580
- DOI 10.14569/IJACSA.2016.070546
- Views 86
- Downloads 0
An Approach to Keep Credentials Secured in Grid Computing Environment for the Safety of Vital Computing Resources
Presently security attacks have aimed to vulnerabilities in repetitive-use authentication secrets like static passwords. The passwords are used by user in clients side are vulnerable, as the attackers can gain access to...
Path Planning in a Dynamic Environment
Path planning is an important area in the control of autonomous mobile robots. Recent work has focused on aspects reductions in processing time than the memory requirements. A dynamic environment uses a lot of memory and...
SentiTFIDF – Sentiment Classification using Relative Term Frequency Inverse Document Frequency
Sentiment Classification refers to the computational techniques for classifying whether the sentiments of text are positive or negative. Statistical Techniques based on Term Presence and Term Frequency, using Support Vec...
Improving Throughput and Delay by Signaling Modification in Integrated 802.11 and 3G Heterogeneous Wireless Network
Current trends show that UMTS network and WLAN will co-exist and work together to support more users with higher data rate services over a wider area. However, this integration invokes many challenges such as mobility ma...
Albanian Sign Language (AlbSL) Number Recognition from Both Hand’s Gestures Acquired by Kinect Sensors
Albanian Sign Language (AlbSL) is relatively new and until now there doesn’t exist a system that is able to recognize Albanian signs by using natural user interfaces (NUI). The aim of this paper is to present a real-time...