  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) varying
the number of key-value pairs and (iv) pooling multiple enrollment utterance
statistics. Experimental results for a 4 task average show that a simple
parameter-free attentive scoring mechanism can improve the average EER by 10%
over the best cosine similarity baseline.
