关键词: 语音情感识别, 梅尔频谱图, 多层感知机, 动态卷积, 注意力机制

Abstract: Speech emotion recognition technology infers the speaker’s emotions by analyzing the vocal signals, enhancing the naturalness and intelligence of human-computer interaction. However, existing models often overlook the semantic information of time and frequency, affecting the recognition accuracy. To address this problem, a multi-layer perceptron model that integrates dynamic convolution and attention mechanisms has been proposed, significantly improving the accuracy of emotion recognition and the efficiency of information utilization. Firstly, the input speech signals are transformed into a Mel-spectrogram to capture detailed signal variations and more accurately reflect human perception of sound, laying the foundation for subsequent feature extraction. The Mel-spectrogram is then tokenized to reduce data complexity. Next, dynamic convolution and split attention mechanisms are employed to extract key temporal-frequency features efficiently. Dynamic convolution adapts to scale changes across different time and frequency domains, thereby enhancing the efficiency of capturing features. Meanwhile, the split attention mechanism enhances the ability of the model to focus on crucial information, effectively improving the feature expressive capability. By combining the advantages of dynamic convolution and split attention mechanisms, the proposed model can fully extract crucial acoustic features, thereby achieving more efficient and accurate emotion recognition. Experiments conducted on the RAVDESS, EmoDB, and CASIA speech emotion databases show that the recognition accuracy of the proposed model significantly surpasses existing technologies, reaching 86.11%, 95.33%, and 82.92%. This verifies the effectiveness of our model in complex emotion recognition tasks, as well as the efficacy of dynamic convolution and attention mechanisms.

Key words: speech emotion recognition, Mel-spectrogram, multi-layer perceptron, dynamic convolution, attention mechanism