Combining evidences from magnitude and phase information using VTEO for person recognition using humming

Abstract

Most of the state-of-the-art�speaker recognition system�use natural speech signal (i.e., real speech, spontaneous speech or contextual speech) from the subjects. In this paper, recognition of a person is attempted from his or her�hum�with the help of machines. This kind of application can be useful to design person-dependent Query-by-Humming (QBH) system and hence, plays an important role in�music information retrieval�(MIR) system. In addition, it can be also useful for other interesting speech technological applications such as human-computer interaction, speech prosody analysis of disordered speech, and speaker forensics. This paper develops new feature extraction technique to exploit�perceptually�meaningful (due to mel frequency warping to imitate human perception process for hearing) phase spectrum information along with magnitude spectrum information from the hum signal. In particular, the structure of state-of-the-art feature set, namely,�Mel Frequency Cepstral Coefficients�(MFCCs) is modified to capture the phase spectrum information. In addition, a new�energy measure, namely,�Variable length�Teager Energy Operator (VTEO) is employed to compute subband energies of different time-domain�subband signals�(i.e., an output of�24�triangular-shaped filters used in the mel filterbank). We refer this proposed feature set as MFCC-VTMP (i.e., mel frequency cepstral coefficients to capture perceptually meaningful magnitude and phase information via VTEO)The polynomial classifier (which is in-principle similar to other discriminatively-trained classifiers such as�support vector machine�(SVM) with polynomial kernel) is used as the basis for all the experiments. The effectiveness of proposed feature set is evaluated and consistently found to be better than MFCCs feature set for several evaluation factors, such as, comparison with other phase-based features, the order of polynomial classifier, person (speaker) modeling approach (such as, GMM-UBM and�i-vector), the dimension of feature vector, robustness under signal degradation conditions, static�vs.�dynamic features, feature discrimination measures and intersession variability.