Abstract: Speech Emotion Recognition (SER) is an important area of research in human–computer interaction that aims to identify human emotions from speech signals. Accurate detection of emotions such as happiness, sadness, anger, fear, and neutrality can significantly enhance applications in virtual assistants, mental health monitoring, and customer service systems. Traditional emotion recognition systems relied on handcrafted acoustic features and conventional machine learning techniques, which often struggled to capture complex patterns in speech data.

In this project, a deep learning–based approach is proposed to automatically recognize emotions from speech signals. The system processes audio inputs by extracting relevant acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), pitch, and energy. These features are then used to train a deep learning model capable of learning emotional patterns from speech data. The proposed model improves classification accuracy by effectively capturing temporal and spectral characteristics of speech signals.

The developed system is evaluated using labeled speech emotion datasets and demonstrates promising performance in recognizing multiple emotional states. The results show that deep learning models can significantly enhance emotion recognition accuracy compared to traditional approaches. This work highlights the potential of speech emotion recognition systems in building more natural and emotionally aware human–machine interactions.

Keywords: Artificial Intelligence, Machine Learning, emotion Detection,

Downloads: | DOI: 10.17148/IARJSET.2026.13375