SSM-Net: A Hybrid Architecture for Child Speech Emotion Recognition

This study proposes SSM-Net, a lightweight hybrid architecture for classifying child speech emotions into positive, neutral, and negative categories. Speech signals are transformed into normalized log-Mel spectrograms and processed through a compact framework that integrates a MobileNetV3-Small backbone, multi-kernel depthwise convolution, and Mamba-based state space modeling. The convolutional modules capture local time-frequency patterns, while the state space blocks model long-range contextual dependencies across the spectrogram representation. The proposed model combines MobileNetV3-Small, multi-kernel depthwise convolution, and Mamba-based sequence modeling for positive, neutral, and negative child speech emotion classification. The main contributions are –

1. A dataset for child emotion detection containing 890 samples has been accumulated and developed.
2. Developing a hybrid lightweight architecture for SER classification into 3 classes such as positive, neutral, and negative called SSM-Net that performs comparatively better than other existing lightweight models.