论文标题
Neuralecho：一个自我煽动的复发性神经网络，用于统一声学回声和言语增强
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
论文作者
论文摘要
声学回声取消（AEC）在全双工语音交流以及在扬声器播放时的条件下识别的前端语音增强起着重要作用。在本文中，我们提出了一个全深度学习框架，该框架隐含地估计了回声/噪声和目标语音的二阶统计数据，并通过基于注意力的复发神经网络共同解决回声和噪声抑制。在客观的语音质量指标，语音识别准确性和模型复杂性方面，提出的模型的表现优于最先进的联合回声取消和语音增强方法F-T-LSTM。我们表明，该模型可以与扬声器嵌入以更好的目标语音增强功能一起使用，并开发一个用于自动增益控制（AGC）任务的分支，以形成一个多合一的前端语音增强系统。
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.
