Multidimensional Speech Feature Extraction for Depression Detection using MDCF-Net

Depression, a mental health illness that affects more than 350 million people worldwide, frequently lacks obvious diagnostic signs, making precise and efficient detection difficult. In this paper, we present MDCF-Net, a unique Speech Emotion Recognition (SER) system that uses multidimensional convolutional neural networks to automatically extract emotional aspects from speech, hence aiding in depression identification. Our model uses both 1D and 2D convolutions to improve the extraction of temporal, spectral, and multi-channel properties from Mel-Frequency Cepstral Coefficients (MFCC). In addition, we use a Cross Attention Transformer and Global Average Pooling (GAP) to improve emotion classification. When tested on several emotional speech datasets, MDCF-Net achieves a stunning 98.51% accuracy on the ESD_Chinese dataset, beating previous algorithms in emotion recognition. Our approach represents a promising development in real-time mental health monitoring via speech analysis.