Abstract
This paper presents use of hum of a person as a biometric cue for person recognition task. Mel Frequency Cepstral Coefficients (MFCC) is found to be state-of-the-art in the voice biometrics. However, it is magnitude-based features and ignores the phase information. This paper shows the effectiveness of phase-based information extracted via Modified Group Delay Function (MODGDF). The features developed by Mel filtering of MODGDF spectrum are called Modified Group Delay Cepstral Coefficients (MGDCC). The paper demonstrates two types of fusion strategies, viz., score-level and feature-level. The experimental results show that overall performance is improved by 3 % if a score-level fusion is employed between MFCC and MGDCC and 19.78 % by feature-level fusion in terms of % Equal Error Rate (EER). These experimental results clearly indicate that incorporating phase information along with magnitude-based features can effectively captures person-specific characteristics in humming.
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