  Acoustic echo cancellation (AEC) plays an important role in the full-duplex
speech communication as well as the front-end speech enhancement for
recognition in the conditions when the loudspeaker plays back. In this paper,
we present an all-deep-learning framework that implicitly estimates the second
order statistics of echo/noise and target speech, and jointly solves echo and
noise suppression through an attention based recurrent neural network. The
proposed model outperforms the state-of-the-art joint echo cancellation and
speech enhancement method F-T-LSTM in terms of objective speech quality
metrics, speech recognition accuracy and model complexity. We show that this
model can work with speaker embedding for better target speech enhancement and
furthermore develop a branch for automatic gain control (AGC) task to form an
all-in-one front-end speech enhancement system.

Audio and Speech Processing;Sound