Speech Emotion Recognition using Mel Frequent Cepstral Coefficients

The ability to articulate one’s research and ideas is dependent on one’s communication skills. When it comes to human dispatches, voice is by far the most common and consequential. Emotional vaticination prediction is the process of determining a speaker’s emotional state from their words. Thus, improved human-computer trade is the result. Predicting someone’s emotional state can be tough due to the subjective nature of emotions and the difficulty of capturing reflection audio, but Speech Emotion Recognition (SER) has made it possible. Increasing the level of understanding and communication between humans and machines is driving research into the field of emotion recognition in speech. People’s emotional states can be gleaned from their tonalities, pitches, facial expressions, behaviours, and other states. Unfortunately, only a small fraction of these is believed to be capable of identifying vocal expressions of emotion. Classifiers are educated to identify emotional tones in spoken language with minimal data