Abstract
Most voice features used in predicting the voice when a person has voted with instability in the vocal fold vibration cause problems in estimating such period; as a result of this challenge, scientists have focused on the development of powerful features independent of pitch estimation. The major goal of this paper is to study and investigate the Acoustic Voice Analysis methods (AVA) based on adaptive features. This investigation will lead to the development of a system of detection. The essential parts of this topic is related to database (described later), sampling the sounds (and satisfying) from the German database with many diseases, degenerative neurological disease (such as chronic inflammation of the larynx and vocal fold nodules). Under the supervision of the used algorithm to accomplish the above task, the Mel-Frequency Cepstral Coefficients (MFCCs with different Jitter and Shimmer), as by likely flux model mixture (GMM) are used in the AVA. MATLAB was used to simulate such a study for the extraction of features as well as making the training and testing process. The achieved results showed that with some kind of analysis, it is possible to find different sound patterns of diseases, e.g. excessive twang, where additional spectral components exist due to the increase in air flow in nasal cavities. Another focal point is some mathematical transformations both in the temporal domain or frequency. These changes can improve the capacity of some voice features voice; however, there is a need to multivariate analysis of parameters which measure the various problems in the process of phonation; after that, it is necessary to analyse the importance of finding and sorting those features that provide more information. Finally, automatic classification of pathological voices was made using any of the known techniques for this purpose. Our achieved results prove that a good classification rate needs efficient features to characterize each class, in this work, on one hand the accuracy of system increases with the number of parameters (best accuracy with 39 coefficients including Jitter & Shimmer) which means that the difference between normal and abnormal become noticeable with second derivate of MFCC and energy more than the others.