PERLUASAN METODE MFCC 1D KE 2D SEBAGAI ESKTRAKSI CIRI PADA SISTEM IDENTIFIKASI PEMBICARA MENGGUNAKAN HIDDEN MARKOV MODEL (HMM)

In this paper, we introduce an extension of Mel-Frequency Cepstrum Coefficients (1D-MFCC) methodology to bispectrum data, referred to as 2D-MFCC, for feature extraction. 2D-MFCC is based on 2D bispectrum data rather than 1D spectrum vector yielded by Fourier transform, so the filter in 1D-MFCC must be extend to 2D filter and using 2D cosine transform to get the mel-cepstrum coefficients from the filtered bispectrum values.  Based on 2D-MFCC, we develop a speaker recognition system with Hidden Markov Model (HMM) as classifier.  The experimental results show that the recognition rate is around 88%, 92% and 99% for 20, 40 and 60 data training, respectively.