Abstract: The present invention provides a method for speech separation and recognition. The present invention overcomes the disadvantages of the existing techniques by providing automatic speech recognition and separation that helps managers see what their service agents and customers are saying. From there, quickly and objectively knowing the wishes and concerns of customers as well as whether their service agents can give accurate and correct advice. In addition, the system is constantly updated based on the semi-supervised training mechanism, which means that the system can self-learn from actual data during operation, thereby helping to improve the system's accuracy.

Abstract: The present invention provides a method of facial expression recognition including 3 steps: step 1: collecting facial expression data, which contributes to solve the problem of lacking data, disparate and bias data, that cause the overfitting problem when training the deep learning model; step 2: designing a new deep learning network that able to focus on special regions of the face to extract and learn the important features of facial expressions by intergating ensemble attention modules into basic deep network architecture like ResNet; step 3: training the ensemble attention deep learning model in step 2 on the collected dataset in step 1, using the combination of two loss functions including ArcFace and Softmax to reduce the overfitting problem.