  In this paper, we study a novel technique that exploits the interaction
between speaker traits and linguistic content to improve both speaker
verification and utterance verification performance. We implement an idea of
speaker-utterance dual attention (SUDA) in a unified neural network. The dual
attention refers to an attention mechanism for the two tasks of speaker and
utterance verification. The proposed SUDA features an attention mask mechanism
to learn the interaction between the speaker and utterance information streams.
This helps to focus only on the required information for respective task by
masking the irrelevant counterparts. The studies conducted on RSR2015 corpus
confirm that the proposed SUDA outperforms the framework without attention mask
as well as several competitive systems for both speaker and utterance
verification.
