Implementation of Machine Learning and Audio Visualizer to Understand the Emotion of a Patient

Abstract

Understanding the emotion of a patient suffered by neurological disorder and etc., is a challenging task. In order to recognize the emotions from them, speech synthesizer is employed to extract the audio and then converted as text if needed. Presently, the application of Signal processing is having a tremendous effect in all disciplines ranging from complex medicinal applications to basic radio signal processing and transmission. The goal of Speech Emotion Recognition (SER) is to identify the emotional components of speech, regardless of its semantic content. While this activity can be successfully carried out by people as a natural element of spoken communication, research into the ability to carry it out automatically using programmable devices is still underway. The goal of studies on automatic emotion detection systems is to develop effective, real-time techniques for identifying emotions in a wide range of human-machine interface users, including call centre agents and consumers, drivers, pilots, and many more. This study will demonstrate a real-time audio visualization method for identifying human emotion based on the frequency of the human voice. Compared to the conventional techniques adopted by numerous researchers in this regard, the proposed model delivered better and precise outcome on recognizing the audio from the patients.