Hierarchical convolutional neural networks with post-attention for speech emotion recognition

Speech emotion recognition (SER) is a key prerequisite for natural human-computer interaction. However, existing SER systems still face great challenges, particularly in the extraction of discriminative and high-quality emotional features. To address this challenge, this study proposes hc-former, a hierarchical convolutional neural network (CNN) with post-attention. Unlike traditional CNNs and recurrent neural networks (RNNs), our model adeptly extracts potent class-discriminative features that integrate spatiotemporal information and longterm dependence. The class-discriminative features extracted by hc-former, which emphasize both interclass separation and intraclass compactness, can more effectively represent different class emotions often confused with one another, leading to superior classification results. Our experimental results further indicate the exceptional performance of hc-former for SER on benchmark datasets, surpassing other peer models in terms of performance while utilizing fewer parameters.