Fast scoring for PLDA with uncertainty propagation

By treating utterances as points in the i-vector space, i-vector/PLDA can achieve fast verification. However, this approach lacks the ability to cope with utterance-length variability. A method called uncertainty propagation (UP) that takes the uncertainty of i-vectors into account has been recently proposed to deal with this problem. However, the loading matrix for modeling utterance-length variability is session-dependent, making UP computationally expensive. In this paper, we demonstrate that utterance-length variability mainly affects the scale of the posterior covariance matrices. Based on this observation, we propose to substitute the session-dependent loading matrices by the ones trained from development data, where the selection of pre-computed loading matrices is based on a fast scalar comparison. This approach can reduce the computation cost of standard UP to the one comparable with the conventional PLDA. Experiments on the NIST 2012 Speaker Recognition Evaluation show that the proposed method can perform as good as the standard UP, but requires only 3.7% of the scoring time. The method also requires substantially less memory as compared with the standard UP, especially when the number of target speakers is large.