Harnessing LSTM Networks for Effective Speech Emotion Recognition

The inability to achieve the desired performance in a correct identification of expressed emotions by using machine learning algorithms sometimes poses challenges for applications like customer service and aviation safety. In this paper, a real-time Speech Emotion Recognition (SER) system based on the LSTM network that recognizes emotion from audio input is introduced. The dataset includes $\mathbf{1, 4 4 0}$ audio recordings with emotions such as calm, happy, sad, fear, disgust, surprise, and neutral. Key to methodology are the selection of a reliable database for emotional speech, feature extraction, such as Melfrequency cepstral coefficients (MFCC), and development of an effective LSTM model. The basic idea behind the model is to enable improved human-computer interaction through the understanding of vocal input analysis and classification so that the system can classify emotions. The proposed study improves emotion recognition accuracy with an overall accuracy ranging around 82.2%. This development is significant, not just in terms of the improvement it brings about in computational linguistics, but it will have practical applications trying to make improvements in user experience with regard to diverse domains, such as customer service and support for mental health services. The future work is mainly aimed at further increasing model accuracy and expanding its capabilities in recognizing other emotions.