Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning

Speech Emotion Recognition (SER) has emerged as a significant research area within Human–Computer Interaction (HCI), enabling intelligent systems to interpret human emotional states from spoken audio. Accurate emotion recognition from speech plays a crucial role in enhancing natural interaction between humans and machines. This paper presents a deep learning–based SER framework that combines Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction with Long Short-Term Memory (LSTM) networks for temporal modelling and emotion classification. MFCC features effectively capture the spectral characteristics of speech signals, whereas LSTM networks are well-suited to modelling long-term temporal dependencies inherent in emotional speech patterns. The proposed model is trained and evaluated on the Toronto Emotional Speech Set (TESS) dataset, which covers multiple emotional categories, including happiness, sadness, anger, fear, and neutrality. Experimental results demonstrate that the proposed MFCC–LSTM approach achieves promising classification accuracy, indicating its effectiveness in recognising emotional states from speech signals. The findings highlight the potential applicability of the proposed system in real-world scenarios, including virtual assistants, call centre analytics, and mental health monitoring systems, thereby contributing to the development of emotion-aware intelligent interfaces.