• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.182-187
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• 2006

This paper proposes a method for boundary recognition of named entity using hidden markov model and ontology information of biological named entity. We uses smoothing method using 31 feature information of word and hierarchical information to alleviate sparse data problem in HMM. The GENIA corpus version 2.1 was used to train and to experiment the proposed boundary recognition system. The experimental results show that the proposed system outperform the previous system which did not use ontology information of hierarchical information and smoothing technique. Also the system shows improvement of execution time of boundary recognition.

• Electronics and Telecommunications Trends
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• v.5 no.2
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• pp.63-77
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• 1990

HMM(Hidden Markov Model)은 음성을 기술하는데 적합한 model이다. 본 고는 최근 CMU에서 개발한 HMM에 기반을 둔 화자독립, 연속음성 system인 SPIHNX에 대하여 기술한다. SPHINX는 단순한 음소의 HMM model을 적용한 baseline SPHINX로부터 시작하여 새로운 지식의 추가 및 음성단위의 조정 등을 통하여 지속적으로 그 성능이 개선되어 왔다. SPHINX의 최종 version은 어휘 약 1000단어 정도의 재원 관리에 관한 질문 형태의 문장을 인식하는데 96%의 높은 인식율을 보인다. SPHINX는 가장 발전된 음성인식 시스템의 하나이며 이는 화자독립, 대용량어휘의 연속음성 인식 시스템의 실현 가능성을 제시한다.

• Journal of Korea Water Resources Association
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• v.46 no.10
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• pp.1029-1040
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• 2013

This study aims to develop a multivariate daily rainfall simulation model considering spatial coherence across watershed. The existing Hidden Markov Model (HMM) has been mainly applied to single site case so that the spatial coherences are not properly addressed. In this regard, HMM coupled with Chow-Liu Tree (CLT) that is designed to consider inter-dependences across rainfall networks was proposed. The proposed approach is applied to Han-River watershed where long-term and reliable hydrologic data is available, and a rigorous validation is finally conducted to verify the model's capability. It was found that the proposed model showed better performance in terms of reproducing daily rainfall statistics as well as seasonal rainfall statistics. Also, correlation matrix across stations for observation and simulation was compared and examined. It was confirmed that the spatial coherence was well reproduced via CLT-HMM model.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.119-128
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• 2008

This paper analyzes the relationship between two representative statistical methods for bandwidth extension (BWE): Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) ones, and compares their performances. The HMM method is a memory-based system which was developed to take advantage of the inter-frame dependency of speech signals. Therefore, it could be expected to estimate better the transitional information of the original spectra from frame to frame. To verify it, a dynamic measure that is an approximation of the 1st-order derivative of spectral function over time was introduced in addition to a static measure. The comparison result shows that the two methods are similar in the static measure, while, in the dynamic measure, the HMM method outperforms explicitly the GMM one. Moreover, this difference increases in proportion to the number of states of HMM model. This indicates that the HMM method would be more appropriate at least for the 'blind BWE' problem. On the other hand, nevertheless, the GMM method could be treated as a preferable alternative of the HMM one in some applications where the static performance and algorithm complexity are critical.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.164-170
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• 2013

Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characteristics of the audio and video streams. A weight coefficient is introduced to adjust the weight of the video and audio streams automatically according to differences in the noise environment. Experimental results show that compared with other bimodal speech recognition approaches, this approach obtains better speech recognition performance.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.127-129
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• 2001

입력된 문장을 분석하여 미리 정해진 범주에 따라 그 문장의 감정 상태의 천이를 출력해 주는 감정인식 시스템을 제안한다. Naive Bayes 알고리즘을 사용했던 이전 방법과 달리 새로 연구된 시스템은 Hidden Markov Model(HMM)을 사용한다. HMM은 특정 분포로 발생하는 현상에서 그 현상의 원인이 되는 상태의 천이를 찾아내는데 적합한 방법으로서, 하나의 문장에 여러 가지 감정이 표현된다는 가정 하에 감정인식에 관한 이상적인 알고리즘이라 할 수 있다. 본 논문에서는 HMM을 사용한 감정인식 시스템에 관한 개요를 설명하고 이전 버전에 비해 보다 향상된 실험결과를 보여준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.336-341
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• 2000

Hidden Markov Model(HMM)을 기반으로 한 얼굴 검출과 얼굴 인식에 대한 프레임작업에 대한 것이다. 관찰 벡터는 Karhunen-Loves Transform(KLT)의 상관관계를 이용하여 얻은 HMM의 정역학 특성을 사용하였으며, 본 연구에서 보여준 얼굴인식 방법은 이전의 HMM 기반의 얼굴인식 방법에서 인식률을 약간 개선함으로써 컴퓨터 연산을 훨씬 간단히 할 수 있음을 보여준다

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.