• 말소리와 음성과학
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• 제4권3호
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• pp.95-101
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• 2012

In this paper, we introduce the automatic speaker identification system 'SPO(Supreme Prosecutors Office) Verifier'. SPO Verifier is a GMM(Gaussian mixture model)-UBM(universal background model) based automatic speaker recognition system and has been developed using Korean speakers' utterances. This system uses a channel compensation algorithm to compensate recording device characteristics. The system can give the users the ability to manage reference models with utterances from various environments to get more accurate recognition results. To evaluate the performance of SPO Verifier on Korean speakers, we compared this system with one of the most widely used commercial systems in the forensic field. The results showed that SPO Verifier shows lower EER(equal error rate) than that of the commercial system.

• 한국멀티미디어학회논문지
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• 제12권1호
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• pp.16-25
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• 2009

안전성을 최우선시 해야 하는 자동차 환경에서 관심지 (POI, Point-Of-Interest) 도메인을 대상으로 하는 대용량 고려 단어 인식 시스템은 최적의 인간-기계 상호접속(HMI, Human-Machine Interface) 기술을 요구하고 있다. 하지만, 매우 제한된 연산처리 능력과 메모리를 가지는 텔레매틱스 단말기에서 10만 단어 이상을 일반적인 음성인식 방식으로 처리하기는 불가능하다. 따라서 본 논문에서는 텔레매틱스 단말기의 관심지 인식을 위하여 다단계 구조의 대용량 고립단어 인식 시스템을 제안하였다. 이 관심지 인식 시스템의 성능향상을 위해 음소별 가우시안 혼합모델(GMM, Gaussian Mixture Model)을 사용한 음소 인식기와 음소별 거리 행렬(PDM, Phoneme-distance Matric) 레빈쉬타인(Levenshtein) 거리를 제안하였다. 제안한 방법은 낮은 처리속도와 적은 양의 메모리를 가지는 텔레매틱스 단말기에서도 대용량 고립단어에 대하여 우수한 인식 성능을 나타내었다. 본 논문에서 제안한 다단계 인식 시스템을 사용하였을 경우 실내에서 최대 94.8%, 자동차환경에서는 최대 92.4%의 인식 성능을 얻을 수 있었다.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.558-570
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2016년도 춘계 종합학술대회 논문집
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• pp.11-12
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• 2016

SVM (Support Vector machine) is powerful machine-learning method, and obtains better performance than traditional methods in the applications of muti-dimension nonlinear pattern classification. For the case of SVM model training and low efficiency in large samples, this paper proposes a combination of statistical parameters of the GMM-UBM (Universal Background Model) model. It is very effective to solve the problem of the large sample for the SVM training. The experiment is carried on four special dynamic hand gestures using the EPIC sensors. And the results show that the improved dynamic hand gesture recognition system has a high recognition rate up to 96.75%.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.67-70
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• 2005

Voice conversion (VC) is a technique for modifying the speech signal of a source speaker so that it sounds as if it is spoken by a target speaker. Most previous VC approaches used a linear transformation function based on GMM to convert the source spectral envelope to the target spectral envelope. In this paper, we propose several nonlinear GMM-based transformation functions in an attempt to deal with the over-smoothing effect of linear transformation. In order to obtain high-quality modifications of speech signals our VC system is implemented using the Harmonic plus Noise Model (HNM)analysis/synthesis framework. Experimental results are reported on the English corpus, MOCHA-TlMlT.

• The Journal of Asian Finance, Economics and Business
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• 제9권1호
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• pp.35-42
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• 2022

The paper aims to examine the impact of exports on the growth of Vietnamese manufacturing small and medium-sized enterprises (SMEs) by exploring the information of 36,053 enterprises across 24 manufacturing sectors from the Vietnam Annual Enterprise Survey (VAES) in the period 2014-2019. To deal with the problem of variable variance, autocorrelation, and endogeneity of the model, the paper uses the OLS regression method with a strong standard error method and system GMM. Export participation by SMEs is positively associated with business growth in terms of sales and total assets, according to the findings. The GMM estimate shows that the rate of sales growth among exporters is 36.5 percent greater than that of non-exporting enterprises in the case of the sales growth measure. Exporters' average total asset growth rate is 19% greater than the rate estimated for non-exporting businesses. The study's findings indicate the need of adopting policies that promote SMEs in transition economies like Vietnam to engage in exporting activities. Furthermore, the findings show that financial assistance and suitable ownership would enable SMEs to take advantage of export opportunities to increase sales and total assets.

• Physical Therapy Rehabilitation Science
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• 제4권1호
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• pp.38-43
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• 2015

Objective: To investigate and compare the size of the rectus femoris (RF), tibialis anterior (TA), and medial gastrocnemius (GMM) using ultrasound (US) imaging in young, elderly, and very elderly groups. Design: Cross sectional study. Methods: This study consisted of 25 young (age 20 years), 24 elderly (age 65-74 years), and 25 very elderly (age 75-90 years) people with no physical dysfunctions. The cross sectional area (CSAs) of the RF and muscle thickness of the TA and GMM were measured at rest and during contraction using an US system. Results: The CSA of the RF and thickness of the TA and GMM were significantly smaller in the elderly and very elderly groups than in the young group (p<0.05). There was a significant difference of the CSA of the RF at rest and during contraction between elderly and very elderly group (p<0.05). In the comparison of the TA and GMM thickness between elderly and very elderly groups, there were no significant differences except for the TA thickness during contraction. There was a significant difference in the percentage change in RF CSA among the three groups (p<0.05). Conclusions: Our results revealed loss of muscle mass in the RF, TA, and GMM in elderly and very elderly people (${\geq}65$ years old). In particular, the greatest age-related decline in muscle mass was observed for the RF. Furthermore, the CSA of the RF declined with aging in the very elderly groups (${\geq}75$ years old).

• 한국통신학회논문지
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• 제39C권10호
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• pp.909-919
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• 2014

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection framework which combines with an adaptive GMM (Gaussian Mixture Model) and a cascade of boosted classifiers to make a faster vehicle LP detector. To develop a background model by using a GMM is possible in the circumstance of a fixed camera and extracts the motions using background subtraction. Firstly, an adaptive GMM is used to find the region of interest (ROI) on which motion detectors are running to detect the vehicle area as blobs ROIs. Secondly, a cascade of boosted classifiers is executed on the blobs ROIs to detect a LP. The experimental results on our test video with the resolution of $720{\times}576$ show that the LPD rate of the proposed system is 99.14% and the average computational time is approximately 42ms.

• 제어로봇시스템학회논문지
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• 제15권9호
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• pp.939-945
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• 2009

In this paper, we proposed a method which corresponds people under the structured spaces with multiple cameras. The correspondence takes an important role for using multiple camera system. For solving this correspondence, the proposed method consists of three main steps. Firstly, moving objects are detected by background subtraction using a multiple background model. The temporal difference is simultaneously used to reduce a noise in the temporal change. When more than two people are detected, those detected regions are divided into each label to represent an individual person. Secondly, the detected region is segmented as features for correspondence by a criterion with the color distribution and context information of human body. The segmented region is represented as a set of blobs. Each blob is described as Gaussian probability distribution, i.e., a person model is generated from the blobs as a Gaussian Mixture Model (GMM). Finally, a GMM of each person from a camera is matched with the model of other people from different cameras by maximum likelihood. From those results, we identify a same person in different view. The experiment was performed according to three scenarios and verified the performance in qualitative and quantitative results.

• International journal of advanced smart convergence
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• 제1권2호
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• pp.39-42
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• 2012

This paper proposed a method that detects emergency situations in a video stream using MHI (Motion History Image) and template matching for a video-based intelligent security system. The proposed method creates a MHI of each human object through image processing technique such as background removing based on GMM (Gaussian Mixture Model), labeling and accumulating the foreground images, then the obtained MHI is compared with the existing MHI templates for detecting an emergency situation. To evaluate the proposed emergency detection method, a set of experiments on the dataset of video clips captured from a security camera has been conducted. And we successfully detected emergency situations using the proposed method. In addition, the implemented system also provides MMS (Multimedia Message Service) so that a security manager can deal with the emergency situation appropriately.