Speech Emotion Recognition using Support Vector Machine

TL;DR

This paper presents a speech emotion recognition system using Support Vector Machine that classifies emotions like sadness, anger, fear, and happiness based on acoustic features, comparing different classification strategies and feature extraction methods.

Contribution

It introduces a SVM-based approach with two classification strategies and compares the effectiveness of LPCC and MFCC features for emotion recognition.

Findings

SVM effectively classifies emotions from speech samples.

Gender-dependent classification improves accuracy.

LPCC and MFCC features show different levels of effectiveness.

Abstract

In this project, we aim to classify the speech taken as one of the four emotions namely, sadness, anger, fear and happiness. The samples that have been taken to complete this project are taken from Linguistic Data Consortium (LDC) and UGA database. The important characteristics determined from the samples are energy, pitch, MFCC coefficients, LPCC coefficients and speaker rate. The classifier used to classify these emotional states is Support Vector Machine (SVM) and this is done using two classification strategies: One against All (OAA) and Gender Dependent Classification. Furthermore, a comparative analysis has been conducted between the two and LPCC and MFCC algorithms as well.