PERLUASAN METODE MFCC 1D KE 2D SEBAGAI ESKTRAKSI CIRI PADA SISTEM IDENTIFIKASI PEMBICARA MENGGUNAKAN HIDDEN MARKOV MODEL (HMM) - Buono, Agus; Jatmiko, Wisnu; Kusumoputro, Benyamin (Makara Journal of Science, 2009)
The Extention of MFCC Technique from 1D to 2D as Feature Extractor for Speaker Identification System Using 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