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

NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement