Speaker identification based on combination of MFCC and UMRT based features
Anett Antony, Rajamma Gopikakumari · Procedia Computer Science · 2018
This paper introduces an isolated word speaker identification system based on a new feature extractor and using Artificial Neural Network. The system is designed for both text independent and text dependent speaker identification system for English words. The speech is recorded using audio wave recorder. Then the preprocessing is applied for the given speech signals. UMRT is a transform which has been used for image compression. Combinations of MFCC and UMRT are taken and are used as a feature extractor. The classification of the features is done using Multi-layer perceptron with back propagation algorithm. The accuracy is taken using confusion matrix. The accuracy achieved is around 97.91% for speech dependent systems while for speech independent system the accuracy is around 94.44%.