• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.561-567
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• 2013

Some of the feature vectors for speech recognition are rare and unusual. These patterns lead to overfitting for the parameters of the speech recognition system and, as a result, cause structural risks in the system that hinder the good performance in recognition. In this paper, as a method of removing these unusual patterns, we try to exclude vectors whose norms are larger than a specified cutoff value and then train the speech recognition system. The objective of this study is to exclude as many unusual feature vectors under the condition of no significant degradation in the speech recognition error rate. For this purpose, we introduce a cutoff parameter and investigate the resultant effect on the speaker-independent speech recognition of isolated words by using FVQ(Fuzzy Vector Quantization)/HMM(Hidden Markov Model). Experimental results showed that roughly 3%~6% of the feature vectors might be considered as unusual, and therefore be excluded without deteriorating the speech recognition accuracy.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.351-357
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• 2017

The purpose of this study is to analyze the relationship between users' expectation and satisfaction through analyzing smart phone voice recognition service and surveying the situational satisfaction of voice recognition service. First, the concept and current status of speech recognition service were investigated through literature research, and the questionnaire survey was carried out through questionnaires based on the second and third principles. As a result, the user is most likely to use the smartphone voice recognition service when making a telephone call. Also, it was found that the service is the most used at home and the service is used most when the hand is not available. Through these results, various methods such as personalization service, condition recognition function, automatic emergency recognition, unlocking by voice could be derived. Based on this research, it is expected that it will be used effectively for improving smartphone voice recognition service and developing wearable device in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.

• 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 Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.213-224
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• 2009

This paper presents a new algorithm for estimating a driver's physical condition using real-time vision system and performs experimentation for real facial image data. The system relies on a face recognition to robustly track the center points and sizes of person's two pupils, and two side edge points of the mouth. The face recognition constitutes the color statistics by YUV color space together with geometrical model of a typical face. The system can classify the rotation in all viewing directions, to detect eye/mouth occlusion, eye blinking and eye closure, and to recover the three dimensional gaze of the eyes. These are utilized to determine the carelessness and drowsiness of the driver. Finally, experimental results have demonstrated the validity and the applicability of the proposed method for the estimation of a driver's physical condition.

• Journal of Fashion Business
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• v.2 no.3
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• pp.98-108
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• 1998

The purpose of this study is to provide for the basic data useful to the effective production and marketing of the foundation garments befitting adult women's body shapes and preferences, and thereby, help them improve their apparel life. For this purpose, 563 Korean adult women aged between 20-59 were sampled to survey their recognition and actual wearing condition of foundation garments and positively identify the factors affecting the practices and thereupon, determine the correlations among them by age group. Data is processed by a computer (SAS) and analyzed by using frequency, percentage, ${\chi}^2$-test, ANOVA, Duncan-test. The main results of this study are as follows; 1. The most of the adult women were aware of the basic underwear terminologies and the absolute majority of them wore brassieres, while a half of them used the girdles. In contrast, only a small portion of them wore body suites or waste-nippers and the absolute minority of them had garterbelts. 2. The use of the foundations depended much on users' ages or seasons. They had begun to wear brassieres and girdles at their age of 15 on average, and bodysuites, waist-nippers or garterbelts around their age of 20 depending on their individual needs. 3. The adult women had 3-6 brassieres and 2-3 girdles on average, but only a minority of them owned one or two body suites, waist-nippers or garterbelts. They had ever used the functional foundations (11.5%) or imported foundations (35.9%), It has been found through this study that adult women's recognition and actual wearing condition of their foundation garments differ by age group, which may well suggest that foundation production need to take such age-wise practices into consideration in setting up their foundation production and marketing strategies.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.257-262
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• 2009

A method of the region extraction and recognition of a speed sign in the real road environment is proposed. The region of speed sign is extracted by using color information and then numbers are segmented in the region. We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. In image sequences of the real road environment, a robust recognition results are achieved with speed signs at normal condition as well as inclined.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.475-483
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• 2012

The characteristic features of speech signal do not vary significantly from frame to frame. Therefore, it is advisable to reduce the redundancy involved in the similar feature vectors. The objective of this paper is to search for the optimal condition of minimum redundancy and maximum relevancy of the speech feature vectors in speech recognition. For this purpose, we realize redundancy reduction by way of a vigilance parameter and investigate the resultant effect on the speaker-independent speech recognition of isolated words by using FVQ/HMM. Experimental results showed that the number of feature vectors might be reduced by 30% without deteriorating the speech recognition accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.52-58
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• 2002

The purpose of condition monitoring and fault diagnosis is to detect faults occurring in machinery in order to improve the level of safety in plants and reduce operational and maintenance costs. The recognition performance is important not only to gain a high recognition rate bur a1so to minimize the diagnosis failures error rate by using off effective rejection module. We examined the problem of performance evaluation for the rejection scheme considering the accuracy of individual c1asses in order to increase the recognition performance. We use the Smith's method among the previous studies related to rejection method. Nearest neighbor classifier is used for classifying the machine conditions from the vibration signals. The experiment results for the performance evaluation of rejection show the modified optimum rejection method is superior to others.