A challenging task remains with regarding to speech emotion recognition due to issues such as emotional corpus problems

association between emotion and acoustic features

and speech emotion recognition modeling. Conventional context-based speech emotion recognition system risks of losing the context details of the label layer and neglecting the difference of the two-level due to solely limited to the feature layer. This paper proposed a Bidirectional Long Short-Term Memory (BLSTM) network with embedded attention mechanism combined with hierarchical context learning model. The model completed the speech emotion recognition task in three phases. The first phase extracted the feature set from the emotional speech

then used the SVM-RFE feature-sorting algorithm to reduce the feature in order to obtain the optimal feature subset and assigned attention weights. The second phase

the weighted feature subset was input into the BLSTM network learning feature layer context to obtain the initial emotional prediction result. The third phase used the emotional value to train another independent BLSTM network for learning label layer context information. According to the information

the final prediction was completed based on the output result of the second phase. The model embedded the attention mechanism to automatically learn to adjust the attention to the input feature subset

introduced the label layer context to associate the feature layer context so as to achieve the hierarchical context information fusion and improve the robustness

and improved the model's ability to model the emotional speech. The experimental results on the SEMAINE and RECOLA datasets showed that both RMSE and CCC were significantly improved than the baseline model.