Forceful speech signal classification requires implementing intricate models along with big data for success, yet operational outcomes contend with ethical challenges coupled with privacy risks and bias problems that emerge during emotional interpretation. Two principal challenges affect the process because of speakers’ variations and noisy backgrounds as well as speech irregularities. The proposed multichannel emotion detection system depends on voice data as its communication method throughout the research stage. Various transfer learning methods employed in speech-based platforms enhance both accuracy and performance of emotional classification. To improve the extraction and classification of features efficiency in speech-based analysis, the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset is subjected to the lightweight discrete Hopfield neural network (LW-DHNNet) technique for emotion recognition. Preprocessing with the BlindDE-blurring-based lightweight Wiener filter (BDE-LWWF), feature extraction with the local and global multi-scale feature fusion branch (LGMS-FFB), classification with the discrete Hopfield neural network (DHNNet), and optimization with the green anaconda optimization algorithm (GAOA) are all included in the suggested emotion recognition system. The research on RAVDESS detected eight distinct emotional states including happy, sad, angry, surprised, disgusted, calm, fearful, and neutral. The proposed attention-based deep learning model (LW-DHNNet) reached outstanding average test accuracy at 98.0% which established its great potential for enhancing automated mental health monitoring systems.