ConMSDMamba: Multi-Scale Dilated Mamba Based on Conformer for Speech Emotion Recognition

Although the Conformer model excels in speech processing, its core self-attention mechanism is limited in capturing multi-scale temporal dynamics and lacks explicit modeling of frequency-domain features, both crucial for Speech Emotion Recognition (SER). To address this, we propose ConMSDMamba, a novel Conformer-based architecture for SER. Specifically, to overcome the single-scale limitation of the original self-attention, we introduce a multi-scale dilated structure with parallel dilated convolutions to capture diverse temporal contexts. We further find that combining this structure with bidirectional Mamba models long-range temporal dependencies more efficiently than multi-head self-attention. Furthermore, to complement the Conformer's time-domain focus, we design a time-frequency convolution module that incorporates a wavelet-based branch for joint time-frequency perception. Experimental results on the widely used IEMOCAP and MELD datasets demonstrate that ConMSDMamba outperforms state-of-the-art methods.