  With recent research advances, deep learning models have become an attractive
choice for acoustic echo cancellation (AEC) in real-time teleconferencing
applications. Since acoustic echo is one of the major sources of poor audio
quality, a wide variety of deep models have been proposed. However, an
important but often omitted requirement for good echo cancellation quality is
the synchronization of the microphone and far end signals. Typically
implemented using classical algorithms based on cross-correlation, the
alignment module is a separate functional block with known design limitations.
In our work we propose a deep learning architecture with built-in
self-attention based alignment, which is able to handle unaligned inputs,
improving echo cancellation performance while simplifying the communication
pipeline. Moreover, we show that our approach achieves significant improvements
for difficult delay estimation cases on real recordings from AEC Challenge data
set.
