An Effective System for Acute Spotting Aberration in the Speech of Abnormal Children Via Artificial Neural Network and Genetic Algorithm

Abstract

Problem statement: In real-world environment, speech signal processing plays a vital role among the research communities. A wide range of researches are carried out in this field for denoising, enhancement and more. Besides the other, stress management is important to identify the spot in which the stress has to be made. Approach: In this study, in order to provide proper speech practice for the abnormal person, their speech is analyzed. Initially, the normal and abnormal person’s speech are obtained with the same set of words. As an initial process, the Mel Frequency Cepstrum Coefficients (MFCC) is extracted from both words and the Principal Component Analysis (PCA) is applied to reduce the dimensionality of the words. From the dimensionality reduced words, the parameters are obtained and then these parameters are utilized to train the ANN which is used to identify the word that is abnormal. After identifying the abnormal word, the acute word is extracted through the thresholding operation and then FFT is computed for the acute word. From this FFT, the parameters are obtained and then these parameters are used in the genetic algorithm for optimization. GA is used to identify the spot in which the speech practice is required for the abnormal person. Results: The proposed system is implemented in the working platform of MATLAB. The performance of the proposed system is tested by generating the dataset for the normal and abnormal female children. Conclusion: In this study, an effective system has been proposed to identify the abnormal word and the spot in which the speech has to be improved also identified.