Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition

Abstract

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).