  Deep learning based techniques have been popularly adopted in acoustic echo
cancellation (AEC). Utilization of speaker representation has extended the
frontier of AEC, thus attracting many researchers' interest in personalized
acoustic echo cancellation (PAEC). Meanwhile, task-decoupling strategies are
widely adopted in speech enhancement. To further explore the task-decoupling
approach, we propose to use a two-stage task-decoupling post-filter (TDPF) in
PAEC. Furthermore, a multi-scale local-global speaker representation is applied
to improve speaker extraction in PAEC. Experimental results indicate that the
task-decoupling model can yield better performance than a single joint network.
The optimal approach is to decouple the echo cancellation from noise and
interference speech suppression. Based on the task-decoupling sequence, optimal
training strategies for the two-stage model are explored afterwards.
