• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (C)
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• pp.475-479
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• 2007

본 논문은 Hidden Markov Model을 사용하여 사람의 특정한 행동을 인식하여 사용자에게 알려주는 지능형 영상 감시 시스템을 제안한다. 본 방법에는 카메라를 통해 입력된 영상에서 사람 영역을 찾은 후 발 영역만을 추출하여 특징이 되는 관측열을 생성한다. 특징 영역은 입력 영상의 각 프레임을 16개의 영역으로 나누어 발바닥이 위치한 곳의 코드를 읽어 사용하고, 인식하고자하는 패턴 행동들에 대해서는 각각의 관측열을 구하고 HMM의 Baum-Welch 알고리즘을 사용하여 학습한다. 인식에는 전향 알고리즘을 사용하여 입력된 행동과 학습된 행동을 확률적으로 비교하므로써 영상 내의 행동이 어떤 패턴 행동인지를 결정하여 출력하도록 한다. 제시된 방법은 복도에서 사람의 특정 행동을 인식하는데 성공적으로 적용될 수 있음을 실험을 통해 확인 하였다.

• 전자공학회논문지B
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• 제28B권7호
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• pp.548-556
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• 1991

In this paper, Korean spoken continuous digits are ercognized using DHMM(Dynamic Hidden Markov Model) and lexical analysis to provide the base of developing voice dialing system. After segmentation by phoneme unit, it is recognized. This system can be divided into the segmentation section, the design of standard speech section, the recognition section, and the lexical analysis section. In the segmentation section, it is segmented using the ZCR, O order LPC cepstrum, and Ai, parameter of voice speech dectaction, which is changed according to time. In the standard speech design section, 19 phonemes or syllables are trained by DHMM and designed as a standard speech. In the recognition section, phomeme stream are recognized by the Viterbi algorithm.In the lexical decoder section, finally recognized continuous digits are outputed. This experiment shiwed the recognition rate of 85.1% using data spoken 7 times of 21 classes of 7 continuous digits which are combinated all of the occurence, spoken by 10 man.

• 한국인터넷방송통신학회논문지
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• 제8권4호
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• pp.67-73
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• 2008

마코브 랜덤 필드로 모델링한 마코브 네트워크에서 신뢰확산 알고리즘은 각각의 화소에 대응하는 노드들 사이의 메시지 이동에 의해 동작한다. 신뢰확산 알고리즘은 정확한 결과를 얻기 위해 많은 수의 반복 연산을 요구하게 된다. 본 논문에서는 다해상도 영역에서 신뢰확산 알고리즘을 적용한 스테레오 정합 알고리즘을 제안한다. 웨이브렛 또는 리프팅에 기반한 다해상도 변환은 스테레오 정합 알고리즘에서 탐색 영역을 줄일 수 있는 장점 갖기 때문에 고속의 연산을 통해 변이 영상을 생성할 수 있다.

• 한국공작기계학회논문집
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• 제13권1호
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• pp.28-34
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• 2004

In this study, new way of evaluating manufacturing cost Is organized and applied. In real manufacturing circumstances, tolerances of parts and assemblies are closely related to the cost. Several researches have been tried to identify the relations and set models. Moreover tolerances have influences on the maintenance of the manufacturing facilities. However Past researches have not considered the processing cost for the failed products. Therefore maintenance costs are represented as stochastic expressions, which include reliability of assembly and facilities. The stochastic nature of the maintenance cost is modeled and solved using Markov chain approach. Results show that this approach gives reliable estimations with remarkable computing time reduction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.236-240
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• 2004

Stroke-based composite HMMs with articulation states are proposed to deal with 3D spatio-temporal trajectory gestures. The direct use of 3D data provides more naturalness in generating gestures, thereby avoiding some of the constraints usually imposed to prevent performance degradation when trajectory data are projected into a specific 2D plane. Also, the decomposition of gestures into more primitive strokes is quite attractive, since reversely concatenating stroke-based HMMs makes it possible to construct a new set of gesture HMMs without retraining their parameters. Any deterioration in performance arising from decomposition can be remedied by a partial tuning process for such composite HMMs.

• 대한의용생체공학회:의공학회지
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• 제27권3호
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• pp.101-109
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• 2006

It has been known that there is strong relationship between human voices and the movements of the articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The EMG signals were acquired from three articulatory facial muscles. Preliminary, 10 Korean digits were used as recognition variables. The various feature parameters including filter bank outputs, linear predictive coefficients and cepstrum coefficients were evaluated to find the appropriate parameters for EMG-based speech recognition. The sequence of the EMG signals for each word is modelled by a hidden Markov model (HMM) framework. A continuous word recognition approach was investigated in this work. Hence, the model for each word is obtained by concatenating the subword models and the embedded re-estimation techniques were employed in the training stage. The findings indicate that such a system may have a capacity to recognize speech signals with an accuracy of up to 90%, in case when mel-filter bank output was used as the feature parameters for recognition.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• 한국철도학회논문집
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• 제12권2호
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• pp.285-290
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• 2009

논문에서는 연속HMM(Continuos Hidden Markov Model)을 이용하여 실시간으로 발매기 명령어(314개 역명)를 인식 할 수 있도록 음성인식 시스템을 구현하였다. 특징 벡터로 39 MFCC를 사용하였으며, 인식률 향상을 위하여 895개의 tied-state 트라이폰 음소 모델을 구성하였다. 시스템 성능 평가 결과 다중 화자 종속 인식률은 99.24%, 다중화자 독립 인식률은 98.02%의 인식률을 나타내었으며, 실제 노이즈가 있는 환경에서 다중 화자 독립 실험의 경우 93.91%의 인식률을 나타내었다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2002년도 하계학술발표대회 논문집 제21권 1호
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• pp.69-72
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• 2002

본 논문에서 화자공간모델 evolution에 기반한 continuous density hidden Markov model (CDHMM)의 online 적응에 대한 새로운 기법을 제안한다. 학습화자의 a priori knowledge을 나타내는 화자공간모델은 factor analysis (FA) 또는 probabilistic principal component analysis (PPCA)와 같은 은닉변수모델(latent variable model)에 의해 효과적으로 나타내어진다. 은닉 변수모델은 화자공간모델뿐아니라 CDHMM 파라메터의 ajoint prior분포를 표시함으로, maximum a posteriori(MAP)적응기법에 직접 적용되어진다. 화자공간모델의 hyperparameters와 CDHMM파라메터를 동시에 순차적으로 적응하기 위해 quasi-Bayes (QB)추정 기술에 기반한 online 적응기법을 제안한다. 연속숫자음 인식과 관련된 화자적응 실험을 통해 제안된 기법은 적은 적응데이터에서 좋은 성능을 나타내며, 데이터가 증가함에 따라 성능이 지속적으로 증가함을 보여준다.

• 음성과학
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• 제13권3호
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• pp.7-18
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• 2006

Diagnosis of pathological voice is one of the important issues in biomedical applications of speech technology. This study focuses on the discrimination of voice disorder using HMM (Hidden Markov Model) for automatic detection between normal voice and vocal fold disorder voice. This is a non-intrusive, non-expensive and fully automated method using only a speech sample of the subject. Speech data from normal people and patients were collected. Mel-frequency filter cepstral coefficients (MFCCs) were modeled by HMM classifier. Different states (3 states, 5 states and 7 states), 3 mixtures and left to right HMMs were formed. This method gives an accuracy of 93.8% for train data and 91.7% for test data in the discrimination of normal and vocal fold disorder voice for sustained /a/.