Accounting for uncertainty of i-vectors in speaker recognition using uncertainty propagation and modified imputation

One of the biggest challenges in speaker recognition is incomplete observations in test phase caused by availability of only short duration utterances. The problem with short utterances is that speaker recognition needs to be handled by having information from only limited amount of acoustic classes. By considering limited observations from a test speaker, the resulting i-vector as a representative of short utterance will be uncertain; the shorter the duration, the higher the uncertainty. In recent studies, an uncertainty decoding technique has been employed in probabilistic linear discriminant analysis (PLDA) modeling in order to account for uncertain i-vectors. In this paper, we propose to extend uncertainty handling using simplified PLDA scoring and modified imputation. We experiment with a state-of-the-art speaker recognition system focusing on uncertainty caused by controlled utterance duration. The uncertainties after i-vector extraction are being propagated through pre-processing steps and both uncertainty decoding and modified imputation are considered. Our experimental results indicate improved equal error rate and detection cost attained by using uncertainty-of-observation techniques in dealing with short duration utterances.