A Hybrid of Genetic Algorithm and Gaussian Mixture Model for Features Reduction and Detection of Vocal Fold Pathology

Subject Areas : B. Computer Systems Organization

Vahid Majidnezhad ¹ , Igor Kheidorov ²

1 - The United Institute of Informatics Problems, National Academy of Science, Minsk, Belarus
2 - The United Institute of Informatics Problems, National Academy of Science, Minsk, Belarus

Received: 2013-09-17 Accepted : 2013-09-17 Published : 2013-05-01

Keywords: Vocal Fold Pathology Diagnosis, Wavelet Packet Decomposition(WPD), Mel-Frequency-Cepstral-Coefficient (MFCC), Principal Component Analysis (PCA), Genetic algorithm (GA), Gaussian Mixture Model (GMM),

Abstract :

Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. In this paper initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis has been presented. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA-based method for feature reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Gaussian Mixture Model (GMM) is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with current methods.