  The emergence of self-supervised representation (i.e., wav2vec 2.0) allows
speaker-recognition approaches to process spoken signals through foundation
models built on speech data. Nevertheless, effective fusion on the
representation requires further investigating, due to the inclusion of fixed or
sub-optimal temporal pooling strategies. Despite of improved strategies
considering graph learning and graph attention factors, non-injective
aggregation still exists in the approaches, which may influence the performance
for speaker recognition. In this regard, we propose a speaker recognition
approach using Isomorphic Graph ATtention network (IsoGAT) on self-supervised
representation. The proposed approach contains three modules of representation
learning, graph attention, and aggregation, jointly considering learning on the
self-supervised representation and the IsoGAT. Then, we perform experiments for
speaker recognition tasks on VoxCeleb1\&2 datasets, with the corresponding
experimental results demonstrating the recognition performance for the proposed
approach, compared with existing pooling approaches on the self-supervised
representation.
