  End-to-End deep learning has shown promising results for speech enhancement
tasks, such as noise suppression, dereverberation, and speech separation.
However, most state-of-the-art methods for echo cancellation are either
classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay
estimator and adaptive linear filter are based on traditional signal processing
concepts, and deep learning algorithms typically only serve to replace the
non-linear residual echo suppressor. This paper introduces an end-to-end echo
cancellation network with a streaming cross-attention alignment (SCA). Our
proposed method can handle unaligned inputs without requiring external
alignment and generate high-quality speech without echoes. At the same time,
the end-to-end algorithm simplifies the current echo cancellation pipeline for
time-variant echo path cases. We test our proposed method on the ICASSP2022 and
Interspeech2021 Microsoft deep echo cancellation challenge evaluation dataset,
where our method outperforms some of the other hybrid and end-to-end methods.
