Odyssey 2014

Text-independent speaker verification can reach high accuracy provided that there are sufficient amount of training and test speech utterances. Gaussian mixture model - universal background model (GMM-UBM), joint factor analysis (JFA) and identity-vector (i-vector) represent the dominant techniques used in this area in view of their superior performance. However, their accuracies drop significantly when the duration of speech utterances are much constrained. In many realistic voice biometric application, the speech duration is required to be quite short, which leads to low accuracy. One solution is to use pass-phrases in place of the uncertain contents. In contrast with text-independent system, this kind of text-dependent speaker verification can achieve higher accuracy even when the speech is short. In this paper, we conduct a study on the application of the pass-phrase based speaker modeling and recognition where the speech signal is obtained through VHF (Very High Frequency) communication channel. We attempt to evaluate the effectiveness of the GMM-UBM, JFA, i-vector methods and their fusion system on this text-dependent speaker verification platform. Our primary target is to achieve equal error rate (EER) of 10~15% under adverse condition using about 3 seconds of speech sample.