• Journal of the Korean Home Economics Association
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• v.27 no.1
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• pp.165-179
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• 1989

The purpose of this study is to examine the difference between wife and husband in the level of stress recognition and distress subsequent to the life events, and thus 5 kinds of study questions have been established for the achievement of this purpose. Data were obtained from 371 couples who resided in Seoul through Likert-Type questionaire, and Frequency, Percentage, Mean, T-Test, One Way ANOVA, Pearson ${\gamma}$ Correlation, Multiple Regression, Cronbach ${\alpha}$ Coefficient were calculated. As the result of this studay, the experiences subsequent to life events, the level stress recognition and distress was different from wife and husband, and it was generally shown that those are higher for wife rather than husband.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1377-1382
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• 2012

In this paper, we propose a novel recognition method that extract source data from encoded signal that are displayed on FND mounted on home appliances. First of all, it find a candidate FND region from sequential difference images taken by smartphone and extract segment image using clustering RGB value. After that, it normalize segment images to correct a slant error and recognize each segments using a relative distance. Experiments show the robustness of the recognition algorithm on smartphone.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.289-290
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• 2007

In order to improve the performance of a speech recognition system in the presence of noise, we propose a noise robust front-end using two-channel speech signals by separating speech from noise based on the computational auditory scene analysis (CASA). The main cues for the separation are interaural time difference (ITD) and interaural level difference (ILD) between two-channel signal. As a result, we can extract 39 cepstral coefficients are extracted from separated speech components. It is shown from speech recognition experiments that proposed front-end has outperforms the ETSI front-end with single-channel speech.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1009-1014
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• 1994

This paper improves the previously proposed spectral mapping method for supervised speaker adaptation in which a mapped spectrum is interpolated from speaker difference vectors at typical spectra based on a minimized distortion criterion. In estimating these difference vectors, it is important to find an appropriate number of typical points. The previous method empirically adjusts the number of typical points, while the present method optimizes the effective number by rank reduction of normal equation. This algorithm was applied to a supervised speaker adaptation for Korean word recognition using the templates form a prototype male speaker. The result showed that the rank reduction technique not only can automatically determine an optimal number of code vectors, but also slightly improves the recognition scores compared with those obtained by the previous method.

• The Korean Journal of Health Service Management
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• v.9 no.3
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• pp.267-276
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• 2015

Objectives : In this study, we were to investigated the recognition of the hospital services by college students with variable majors. Methods : In survey questionnaire included were 9 general questions, 18 items recognition of hospital services in questionnaire. For the statistical analysis of this study, we were used the IBM SPSS 21.0; t-test, ANOVA and multiple regression analysis were performed. Results : (1) The general characteristics of perception was as follows; recognition of hospital services in accordance with grade showed significant differences.(F=2.638, p<0.05) (2) Effect of the recognition was analyzed from the general characteristics; F value is 2.638(p<0.05) and 3.678(p<0.01) as shown; the regression model showed a significant expression. Conclusions : In this study, we observed the perception of the hospital service by college students. As a result, we could not see a statistically significant difference. These results can be interpreted as a show the direction of health education in the future.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.290-291
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• 2008

The most popular speech feature used in speech recognition today is the Mel-Frequency Cepstral Coefficients (MFCC) algorithm, which could reflect the perception characteristics of the human ear more accurately than other parameters. This paper adopts MFCC and its first order difference, which could reflect the dynamic character of speech signal, as synthetical parametric representation. Furthermore, we quote Dynamic Time Warping (DTW) algorithm to search match paths in the pattern recognition process. We use the software "GoldWave" to record English digitals in the lab environments and the simulation results indicate the algorithm has higher recognition accuracy than others using LPCC, etc. as character parameters in the experiment for Digital Isolated Word Recognition (DIWR) system.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1105-1112
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• 2020

Gait recognition, as a promising biometric, can be used in video-based surveillance and other security systems. However, due to the complexity of leg movement and the difference of external sampling conditions, gait recognition still faces many problems to be addressed. In this paper, an improved convolutional neural network (CNN) based on Gabor filter is therefore proposed to achieve gait recognition. Firstly, a gait feature extraction layer based on Gabor filter is inserted into the traditional CNNs, which is used to extract gait features from gait silhouette images. Then, in the process of gait classification, using the output of CNN as input, we utilize metric learning techniques to calculate distance between two gaits and achieve gait classification by k-nearest neighbors classifiers. Finally, several experiments are conducted on two open-accessed gait datasets and demonstrate that our method reaches state-of-the-art performances in terms of correct recognition rate on the OULP and CASIA-B datasets.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.905-913
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• 1987

This paper presents a speaker dependent isolated digit recognition algorithm using the Mellin transform. Since the Mellin transform converts a scale information into a phase information, attempts have been made to utilize this scale invariance property of the Mellin transform in order to alleviate a time-normalization procedure required for a speech recognition. It has been found that good results can be obtained by taking the Mellin transform to the features such as a ZCR, log energy, normalized autocorrelation coefficients, first predictor coefficient and normalized prediction error. We employed a difference function for evaluating a similarity between two patterns. When the proposed algorithm was tested on Korean digit words, a recognition rate of 83.3% was obtained. The recognition accuracy is not compatible with the other technique such as LPC distance however, it is believed that the Mellin transform can effectively perform the time-normalization processing for the speech recognition.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.6
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• pp.760-771
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• 1998

The purpose of this study is to clarify the concept and construct of the problem recognition stage in consumer decision making process of apparel Purchase. This study was supplemented by the theoretical study and field interviews. 40 women were interviewed on their apparel purchase situation to identify problem recognition process. As a result, the concept of problem recognition in apparel purchase is the perceived difference between the ideal state of apparel affairs and the actual situation sufficient to arouse and activate the decision making process. And the problem recognition stage in apparel purchase is constituted of the following steps: gestation, categorization, problem definition, and purchase intention formation. In most cases, these four steps existed, but in some cases several steps were deleted or condensed.

• ETRI Journal
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• v.46 no.1
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• pp.71-81
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• 2024

Recent advances in deep learning for speech and visual recognition have accelerated the development of multimodal speech recognition, yielding many innovative results. We introduce a Korean audiovisual speech recognition corpus. This dataset comprises approximately 150 h of manually transcribed and annotated audiovisual data supplemented with additional 2000 h of untranscribed videos collected from YouTube under the Creative Commons License. The dataset is intended to be freely accessible for unrestricted research purposes. Along with the corpus, we propose an open-source framework for automatic speech recognition (ASR) and audiovisual speech recognition (AVSR). We validate the effectiveness of the corpus with evaluations using state-of-the-art ASR and AVSR techniques, capitalizing on both pretrained models and fine-tuning processes. After fine-tuning, ASR and AVSR achieve character error rates of 11.1% and 18.9%, respectively. This error difference highlights the need for improvement in AVSR techniques. We expect that our corpus will be an instrumental resource to support improvements in AVSR.