Wavelet Packet Based Mel Frequency Cepstral Features for Text Independent Speaker Identification

The present research proposes a paradigm which combines the Wavelet Packet Transform (WPT) with the distinguished Mel Frequency Cepstral Coefficients (MFCC) for extraction of speech feature vectors in the task of text independent speaker identification. The proposed technique overcomes the single resolution limitation of MFCC by incorporating the multi resolution analysis offered by WPT. To check the accuracy of the proposed paradigm in the real life scenario, it is tested on the speaker database by using Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) as classifiers and their relative performance for identification purpose is compared. The identification results of the MFCC features and the Wavelet Packet based Mel Frequency Cepstral (WP-MFC) Features are compared to validate the efficiency of the proposed paradigm. Accuracy as high as 100% was achieved in some cases using WP-MFC Features.