Emotion recognition through voice by MLP classifier and Deep Sequential Neural Network

Thesis type
(Thesis) M.A.Sc.
Date created
2021-12-16
Authors/Contributors
Abstract
This thesis aspires to provide a thorough study on Speech Emotion Recognition in the field of Machine Learning. The main objective is to simplify the path towards emotion recognition by voice without sacrificing efficiency. In other words, the presented thesis studies the limits of least computationally complex algorithms in SER. By the end of this study, a traditional method which is MLP classifier and a Deep Learning approach with Deep Sequential Neural Network are compared. The algorithms use RAVDESS for training. Different combinations of three features, MFCC, Mel-spectrogram, and Chroma are utilized in both algorithms to determine the most efficient combination.
Document
Identifier
etd21755
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Rad, Ahmad
Language
English
Attachment Size
input_data\22425\etd21755.pdf 4.21 MB