Speech enhancement method using context-sensitive attention mechanism and recurrent neural network

In order to make full use of context information to enhance speech,a speech enhancement method using context-sensitive attention mechanism and recurrent neural network is proposed.Firstly,in the training phase,a multi-layer perceptron for calculating attention weights and a deep recurrent neural network for enhancing speech are jointly trained,and in the test phase,the attention vector of each frame is calculated and spliced with this frame,then fed the concatenated frame into the deep recurrent network to realize speech enhancement.In the experiments with different signal-to-noise ratios,our method can improve speech quality and intelligibility better than the baseline model.At-6 dB,STOI (Short-Time Objective Intelligibility) and PESQ (Perceptual Evaluation of Speech Quality) can be increased by 0.16 and 0.77 respectively compared with the noisy speech.At the same time,the performance of the method is still optimal or near optimal under the condition of unknown noise.Therefore,the introduction of the attention mechanism can effectively strengthen the ability to use context information of the model,thus improving its enhanced performance.