A new approach for speaker and accent recognition based on wavelets, namely Discrete Wavelet Packet (DWPT), Dual Tree Complex Wavelet Packet Transform (DT-CWPT) and Wavelet Packet Transform (WPT) based non-linear features are investigated. The results are compared with conventional MFCC and LPC features. k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifier are used to quantify the speaker and accent recognition rate. The database for the research was developed using English digits (0~9) and Malay words. The highest accuracy for speaker recognition obtained is 93.54% while for accent recognition; it is 95.86% using Malay words. Combination of features for speaker recognition is obtained from ELM classifier is 98.68 % and for accent recognition is 98.75 % using Malay words.
