Person Recognition Using Humming, Singing and Speech

Speaker recognition deals with designing the system which recognizes the person by speech with the help of computers. In this paper, the various biometric signals produced by humans, viz., speech, singing and humming are considered for person recognition task. Corpus has been developed from 28 subjects in real-life settings. For person recognition task, state-of-the-art feature set, viz., Mel Frequency Cepstral Coefficients (MFCC) and a discriminatively-trained polynomial classifier of 2^nd order approximation are used as spectral feature and classification techniques, respectively. Our experimental results indicate that the performance of person recognition system obtained using humming outperforms other biometric patterns (i.e., speech and singing) by 9 % in EER and 9 % in Identification Rate. We believe that this may be due to the person-specific characteristics are better captured in humming sounds, (which are nasalized sounds) than speech and singing.