• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.107-114
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• 2008

In this paper, we constructed a speech emotion recognition system that classifies four emotions - neutral, happy, sad, and anger from speech based on male/female gender discrimination. At first, the proposed system distinguish between male and female from a queried speech, then the system performance can be improved by using separate optimized feature vectors for each gender for the emotion classification. As a emotion feature vector, this paper adopts ZCPA(Zero Crossings with Peak Amplitudes) which is well known for its noise-robustic characteristic from the speech recognition area and the features are optimized using SFS method. For a pattern classification of emotion, k-NN and SVM classifiers are compared experimentally. From the computer simulation results, the proposed system was proven to be highly efficient for speech emotion classification about 85.3% regarding four emotion states. This might promise the use the proposed system in various applications such as call-center, humanoid robots, ubiquitous, and etc.

• Journal of the Korea Convergence Society
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• v.8 no.7
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• pp.7-13
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• 2017

Robot performances in public places are conducted for the purpose of promoting robot technology and inducing interest in events, exhibitions, and streets instead of dedicated stages. This paper extracts robot images in real time from a robot operation in front of a green chroma key cloth, and synthesizes them on various stage images. A simple and robust method for extracting a foreground robot from a chroma key background without a user's preset is proposed. After increasing the color difference between the background and the foreground, this method automatically removes the background based on the histogram of the difference information, thereby eliminating the need for a user's preset. The simulation shows 98.8% of foreground extraction rate and experimental results demonstrate that the robots can effectively be extracted from the background.