Abstract
A new feature extraction method is presented in this paper. It is modification to our previous work where we have extracted Multidimentional local features (MDLF). We name the new feature as Multi-Directional Local Feature with moving average(MDLF-Mavg). MDLF-Mavg is based on three-point linear regression and three point moving average. Linear regression is applied on horizontal (time axis) that captures phoneme onset and offset and vertical (frequency axis) which capture formant contours whereas modified moving average is applied on (45 degree) time-frequency axis and (135 degree) time-frequency axis which capture the voiceprint of speaker. The MDLF-Mavg performance is compared to other speech features in a speaker recognition system. MDLF-Mavg has shown better performance than the other features. In the case of the female only part of the database it achieved 100% recognition rate. We will show that MDLF-Mavg produce what can be looked at as a voice print for each speaker when vocalizing a certain text.