• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.769-776
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• 2010

The relationship between morphological characteristics and sensibility of hands for beautiful hands were investigated for college woman in early twenties. Hand Emotional Quotient (HEQ) which is classified as 10 grades was newly suggested as one of the sensibility evaluation methods for the beautiful hands and was applied to assess the qualitative and quantitative relationships between morphological characteristics and HEQ. The factors influenced on the HEQ were in order as width-length ratio of hands, finger length-hand length and finger width-hand length ratios. Otherwise the influences of hand length-nail length and finger length-nail length ratios on HEQ were insignificant. The longer length of hand, finger, and nail and the narrower width of hand and finger, the higher HEQ hands get. In conclusion, it is found that all morphologic characteristics of hands influenced on the sensitivity synthetically. The data constructed on the hand's sensibility is very useful for various beauty products and service areas related to hands.

• MALSORI
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• no.60
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• MALSORI
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• v.55
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• pp.131-142
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• 2005

A mismatch between the training and the test conditions often causes a drastic decrease in the performance of the speech recognition systems. In this paper, non-linear transformation techniques based on histogram equalization in the acoustic feature space are studied for reducing the mismatched condition. The purpose of histogram equalization(HEQ) is to convert the probability distribution of test speech into the probability distribution of training speech. While conventional histogram equalization methods consider only the probability distribution of a test speech, for noise-corrupted test speech, its probability distribution is also distorted. The transformation function obtained by this distorted probability distribution maybe bring about miss-transformation of feature vectors, and this causes the performance of histogram equalization to decrease. Therefore, this paper proposes a new method of calculating noise-removed probability distribution by using assumption that the CDF of noisy speech feature vectors consists of component of speech feature vectors and component of noise feature vectors, and this compensated probability distribution is used in HEQ process. In the AURORA-2 framework, the proposed method reduced the error rate by over $44\%$ in clean training condition compared to the baseline system. For multi training condition, the proposed methods are also better than the baseline system.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.68-74
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• 2014

In a previous work, we proposed a novel approach of histogram equalization using a supplement set which is composed of centroids of Fuzzy C-Means of the background utterances. The performance of the proposed method is affected by the size of the supplement set, but it is difficult to find the best size at the point of recognition. In this paper, we propose a histogram equalization using a supplement set for majority voting based speaker identification. The proposed method identifies test utterances using a majority voting on the histogram equalization methods with various sizes of supplement sets. The proposed method is compared with the conventional feature normalization methods such as CMN(Cepstral Mean Normalization), MVN(Mean and Variance Normalization), and HEQ(Histogram Equalization) and the histogram equalization method using a supplement set.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.79-86
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• 2012

In this paper, we propose a novel approach to improve histogram equalization for speaker identification. Our method collects all speech features of UBM training data to make a reference distribution. The ranks of the feature vectors are calculated in the sorted list of the collection of the UBM training data and the test data. We use the ranks to perform order-based histogram equalization. The proposed method improves the accuracy of the speaker recognition system with short utterances. We use four kinds of speech databases to evaluate the proposed speaker recognition system and compare the system with cepstral mean normalization (CMN), mean and variance normalization (MVN), and histogram equalization (HEQ). Our system reduced the relative error rate by 33.3% from the baseline system.

• Korean Journal of Child Education & Care
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• v.17 no.2
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• pp.99-114
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• 2017

This study investigated the affects of Parent-child interaction, peer interaction and teacher-child interaction on kindergartners' social competence, language and literacy Ability. Participants were 1203 children attending panel study on Korean children. It used the 'Social Competence Inventory', 'SECCYD', 'HEQ', 'PIPPS' and the 'Teacher-Child Relation Scale' in collection of data. Collected data were analyzed through the SPSS 18.0 program and pearson's correlation and step-wise multi regression analysis. Findings are as follows. First, there were positive correlations between children's social competence and parent-child interaction, peer interaction and teacher-child interaction. Second, there were positive correlations between children's language and literacy ability and peer interaction and teacher-child interaction. Third, Parent-child interaction, peer interaction were predictable variable affecting the young children's social competence. Fourth, peer interaction and teacher-child interaction were found to be predictable variables affecting the young children's language and literacy ability.

• Nuclear Engineering and Technology
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• v.27 no.5
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• pp.775-783
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• 1995

This study is to assess the effects of increasing wall thickness on the safety margin of pressure tube in operating and of lowering initial hydrogen concentration on the DHC growth in respect to the improvement of the reliability of pressure tube in CANDU reactors. The pressure tube with thicker wall of 5.2 mm shows much higher safety margin for flaw tolerance by 25％ than the current 4.2mmm tube. The thicker pressure tubes have a great benefit in LBB assessment including the initial crack depth at which DHC occurs, the crack length at onset of leaking and the available time for action. The resistance for the pressure tube ballooning at LOCA accident is also increased with the thicker tube. The calculations for Heq concentration after 20 years of operation as a function of wall thickness and initial hydrogen concentration show that the 5.2 mm nil thickness tube with 5 ppm initial hydrogen concentration is the most resistant to DHC. with the lower initial hydrogen concentration, TSS temperature for the precipitation or hydride decreases and the crack growth during cooldown reduces.