Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%