Experimental Design of Speech Emotion Recognition with Multi-task Teacher-student Model

Aiming at the research hotspots of speech emotion recognition in human-computer intelligent interaction, the noisy speech emotion recognition with the multi-task constrained teacher-student model is designed as a research-oriented teaching experiment, and the guiding role of the teacher model, the learning process of the student model and the binding force of multi-level reinforcement loss are observed. The teacher-student model based on Wav2vec 2.0 and the multi-level reinforcement loss mechanism are designed, and then the speech enhancement auxiliary task is introduced into the student model, so that the student model can obtain the feature representation ability of the teacher model through learning. In the testing phase, the student model directly extracts key emotional features from noisy speech for emotion classification. Finally, a large number of experiments are conducted to analyze the performance and robustness of the emotion recognition system. The experimental design based on the teacher-student model helps to improve students' thinking ability, scientific research innovation and exploration awareness.