Accounting for uncertainty of i-vectors in speaker recognition using uncertainty propagation and modified imputation
Rahim Saeidi, Paavo Alku · 2015
One of the biggest challenges in speaker recognition is incom-plete 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 in-formation from only limited amount of acoustic classes. By considering limited observations from a test speaker, the re-sulting 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 simpli-fied 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 uncer-tainties after i-vector extraction are being propagated through pre-processing steps and both uncertainty decoding and modi-fied imputation are considered. Our experimental results indi-cate improved equal error rate and detection cost attained by us-ing uncertainty-of-observation techniques in dealing with short duration utterances. Index Terms: speaker verification, duration, uncertainty de-coding, modified imputation