Deep Learning Approach towards Emotion Recognition Based on Speech

Abstract

Feelings are incredibly vital in the internal actuality of humans. It's a means of communicating one's point of view or emotional condition to others [5]. The birth of the speaker's emotional state from his or her speech signal is appertained to as Speech Emotion Recognition (SER) [2]. There are a many universal feelings that any intelligent system with finite processing coffers can be trained to honour or synthesize as demanded, including Neutral, wrathfulness, Happiness, and Sadness. Because both spectral and prosodic traits contain emotional information, they're utilized in this study for speech emotion identification. One of the spectral parcels is Mel- frequency cepstral portions (MFCC). Prosodic variables similar as abecedarian frequency, loudness, pitch, and speech intensity, as well as glottal factors, are utilized to model colorful feelings. For the computational mapping between feelings and speech patterns, possible features are recaptured from each utterance. The named features can be used to identify pitch, which can also be used to classify gender. In this study, the gender is classified using a Support Vector Machine (SVM) on Ravdess dataset. The Radial Base Function and Back Propagation Network are used to honour feelings grounded on specified features, and it has been shown that the radial base function produces more accurate results for emotion recognition than the reverse propagation network.