  Initially developed for natural language processing (NLP), Transformer model
is now widely used for speech processing tasks such as speaker recognition, due
to its powerful sequence modeling capabilities. However, conventional
self-attention mechanisms are originally designed for modeling textual sequence
without considering the characteristics of speech and speaker modeling.
Besides, different Transformer variants for speaker recognition have not been
well studied. In this work, we propose a novel multi-view self-attention
mechanism and present an empirical study of different Transformer variants with
or without the proposed attention mechanism for speaker recognition.
Specifically, to balance the capabilities of capturing global dependencies and
modeling the locality, we propose a multi-view self-attention mechanism for
speaker Transformer, in which different attention heads can attend to different
ranges of the receptive field. Furthermore, we introduce and compare five
Transformer variants with different network architectures, embedding locations,
and pooling methods to learn speaker embeddings. Experimental results on the
VoxCeleb1 and VoxCeleb2 datasets show that the proposed multi-view
self-attention mechanism achieves improvement in the performance of speaker
recognition, and the proposed speaker Transformer network attains excellent
results compared with state-of-the-art models.
