In earlier days, people used speech as a means of communication or the way a listener is conveyed by voice or expression. But the idea of machine learning and various methods are necessary for the recognition of speech in the matter of interaction with machines. With a voice as a bio-metric through use and significance, speech has become an important part of speech development. In this article, we attempted to explain a variety of speech and emotion recognition techniques and comparisons between several methods based on existing algorithms and mostly speech-based methods. We have listed and distinguished speaking technologies that are focused on specifications, databases, classification, feature extraction, enhancement, segmentation and process of Speech Emotion recognition in this paper

Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate and love. The key elements for the implementation of speech emotion recognition are voice processing, and once the emotion is recognized, the machine interface automatically detects the actions by buzzer and LED. The proposed system is trained and tested on various benchmark datasets, i.e., Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) database, Acoustic-Phonetic Continuous Speech Corpus (TIMIT) database, Emotional Speech database (Emo-DB) database and evaluated based on various parameters, i.e., accuracy, error rate, and time. While comparing with existing technologies, the proposed algorithm gave a better error rate and less time. Error rate and time is decreased by 19.79%, 5.13 s. for the RAVDEES dataset, 15.77%, 0.01 s for the Emo-DB dataset and 14.88%, 3.62 for the TIMIT database. The proposed model shows better accuracy of 81.02% for the RAVDEES dataset, 84.23% for the TIMIT dataset and 85.12% for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM) and Support Vector Machine (SVM) Model.