  This paper presents a novel design of attention model for text-independent
speaker verification. The model takes a pair of input utterances and generates
an utterance-level embedding to represent speaker-specific characteristics in
each utterance. The input utterances are expected to have highly similar
embeddings if they are from the same speaker. The proposed attention model
consists of a self-attention module and a mutual attention module, which
jointly contributes to the generation of the utterance-level embedding. The
self-attention weights are computed from the utterance itself while the
mutual-attention weights are computed with the involvement of the other
utterance in the input pairs. As a result, each utterance is represented by a
self-attention weighted embedding and a mutual-attention weighted embedding.
The similarity between the embeddings is measured by a cosine distance score
and a binary classifier output score. The whole model, named Dual Attention
Network, is trained end-to-end on Voxceleb database. The evaluation results on
Voxceleb 1 test set show that the Dual Attention Network significantly
outperforms the baseline systems. The best result yields an equal error rate of
1:6%.
