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Discriminative Feature Vector Selection for Emotion Classification Based on Speech
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 Title & Authors
Discriminative Feature Vector Selection for Emotion Classification Based on Speech
Choi, Ha-Na; Byun, Sung-Woo; Lee, Seok-Pil;
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 Abstract
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.
 Keywords
Bhattacharrya distance;Pitch;MFCC;LPC;LPCC;Emotion classification;
 Language
Korean
 Cited by
1.
A Design of Dangerous Sound Detection Engine of Wearable Device for Hearing Impaired Persons, The Transactions of The Korean Institute of Electrical Engineers, 2016, 65, 7, 1263  crossref(new windwow)