  Uncertainty modeling in speaker representation aims to learn the variability
present in speech utterances. While the conventional cosine-scoring is
computationally efficient and prevalent in speaker recognition, it lacks the
capability to handle uncertainty. To address this challenge, this paper
proposes an approach for estimating uncertainty at the speaker embedding
front-end and propagating it to the cosine scoring back-end. Experiments
conducted on the VoxCeleb and SITW datasets confirmed the efficacy of the
proposed method in handling uncertainty arising from embedding estimation. It
achieved improvement with 8.5% and 9.8% average reductions in EER and minDCF
compared to the conventional cosine similarity. It is also computationally
efficient in practice.
