  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.
