Parameter-Free Attentive Scoring for Speaker Verification. (arXiv:2203.05642v1 [cs.SD])

This paper presents a novel study of parameter-free attentive scoring for
speaker verification. Parameter-free scoring provides the flexibility of
comparing speaker representations without the need of an accompanying
parametric scoring model. Inspired by the attention component in Transformer
neural networks, we propose a variant of the scaled dot product attention
mechanism to compare enrollment and test segment representations. In addition,
this work explores the effect on performance of (i) different types of
normalization, (ii) independent versus tied query/key estimation, (iii) …

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