Publisher : The Korean Institute of Electrical Engineers
DOI : 10.5370/KIEE.2015.64.9.1363
Title & Authors
Discriminative Feature Vector Selection for Emotion Classification Based on Speech Choi, Ha-Na; Byun, Sung-Woo; Lee, Seok-Pil;
Recently, computer form were smaller than before because of computing technique`s development and many wearable device are formed. So, computer`s cognition of human emotion has importantly considered, thus researches on analyzing the state of emotion are increasing. Human voice includes many information of human emotion. This paper proposes a discriminative feature vector selection for emotion classification based on speech. For this, we extract some feature vectors like Pitch, MFCC, LPC, LPCC from voice signals are divided into four emotion parts on happy, normal, sad, angry and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification.