Abstract A hidden markov model (HMM)-universal background model (UBM) algorithm for the voice print password system is proposed. Due to the sparseness of the enrollment data in the voice print password system, a mono-phone HMM-UBM is firstly trained by using the speaker-independent database. Then, the hypothesized speaker model is obtained by adapting the parameters of the UBM using the speaker’s training speech and the maximum a posteriori (MAP) estimation. The data sparseness problem is thus solved. The equal error rate (EER) of the proposed system is 6.8% on the IFIY-DESKTOP Ⅱ database.
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