Speech Emotion Recognition Using LSTM and MFCC features

Speech is utilized in human-machine connection and serves as a signal of human involvement. The Speech Emotion Recognition (SER) system is a novel form of this interactive system. Sufficient intelligence is provided by the SER to facilitate effective human-computer interaction. Based on the speaker's words, the SER system classifies emotions into groups such as "neutral," "calm," "happy," "sad," "angry," "fearful," "disgust," and "surprise." Languages and machine learning models suitable for SER are defined in this paper. Deep learning is used by this system to effectively classify and learn from multidimensional data. Primary results for a system using the LSTM algorithm and MFCC feature tools are also presented in this work. For the simplicity of user engagement, we have then implemented this model as a website through the usage of a third party.