Syllable Lattice Based Re-Scoring For Speaker Verification

The Gaussian mixture based GMM-UBM approaches have shown good performance in speaker verification without using contextual information. In this paper, we exploit the information provided in the arcs of a decoded syllable lattice for speaker verification. The forward algorithm is used to summarize this information in the syllable lattice instead of the best decoded string. The performance is evaluated on a Mandarin Chinese database. With two minutes of target speaker's enrollment data, the proposed algorithm shows 1.03% of equal-error rate for short input utterances with an average duration of two seconds. By combining with the GMM-UBM, the system shows a 0.74% of equal-error rate.