• Speech Sciences
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• v.7 no.4
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• pp.11-26
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• 2000

In this paper, we propose the HMM with the MIN module. Because initial and re-estimated variance vectors are important elements for performance in HMM recognition systems, we propose a method which compensates for the mismatched statistical feature of training and test data. The MIN module function is a differentiable function similar to the sigmoid function. Unlike a continuous density function, it does not include variance vectors of the data set. The proposed hybrid HMM/MIN module is a unified network in which the observation probability in the HMM is replaced by the MIN module neural network. The parameters in the unified network are re-estimated by the gradient descent method for the Maximum Likelihood (ML) criterion. In estimating parameters, the variance vector is not estimated because there is no variance element in the MIN module function. The experiment was performed to compare the performance of the proposed HMM and the conventional HMM. The experiment measured an isolated number for speaker independent recognition.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Journal of Digital Convergence
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• v.12 no.7
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• pp.273-278
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• 2014

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.145-146
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• 2019

SDN(Software Defined Networking) 환경에서는 OpenFlow 프로토콜을 사용함으로써, 컨트롤러는 스위치가 패킷의 도착이나 Table의 상태에 따라 미리 Flow table의 Entry를 추가, 갱신, 삭제하도록 제어한다. 본 논문에서는 Flow entry의 사용량에 대한 확률을 정확하게 측정하기 위하여 Hidden Markov Mode (HMM)을 적용한 새로운 Flow entry 사전 제거 기법을 제안한다. 본 연구를 통해 HMM을 사용하여 기존 기술들보다 효과적이며 Flow table 관리에 있어 향상된 성능을 목표로 한다.

• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.25-30
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• 2004

In this Paper, a beat recognition method from a sequence of images of conducting person was proposed. Hand position was detected using color discrimination, and symbolized by quantization. Then a motion of the conductor was represented as a sequence of symbols. HMM (Hidden Markov Model), which is excellent for recognition of sequence pattern with some level of variation, was used to recognize the sequence of symbols to be a motion for a beat.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.75-76
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• 2001

An endpoint detection method for speech signals utilizing hidden Markov model(HMM) is proposed. It turns out that the proposed algorithm is quite satisfactory to apply isolated word speech recognition.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.291-295
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• 1997

현재에 주로 개발되어 상용화가 시작되고 있는 음성인식 시스템의 대부분은 단어인식을 기분으로 하는 시스템으로 적용 단어수를 늘려줌으로서 인식범위를 늘일 수 있으나, 그에 따라 검색해야하는 단어수가 늘어남으로서 전체적인 시스템의 속도 및 성능이 저하되는 경향이 있다. 이러한 단점의 극복을 위하여 본 논문에서는 HMM(Hidden Markov Model)과 GA(Genetic Algorithm)를 이용한 한국어 음성의 음소단위 인식 시스템을 구현하였다. 음성 특징으로는 LPC Cepstrum 계수를 사용하였으며, 인식시는 인식대상이 되는 단어에 대하여 GA(Genetic Algorithm)을 통하여 각 음소를 분리하고, 음소단위로 학습된 HMM 파라미터를 적용하여 인식함으로써 각각의 음소별 가능하도록 하는 방법을 제안하였다.

• The Journal of the Acoustical Society of Korea
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• v.10 no.2
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• pp.29-35
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• 1991

In this paper, we discuss the method of recognizing allophone unit isolated words using hidden Markov model(HMM). Frist we constructed allophone lexicon by extracting allophones from training data and by training allophone HMMs. And then to recognize isolated words using allophone HMMs, it is necessary to construct word dictionary which contains information of allophone sequence and inter-allophone transition probability. Allophone sequences are represented by allophone HMMs. To see the effects of inter-allophone transition probability and to determine optimal probabilities, we performend some experiments. And we showed that small number of traing data and simple train procedure is needed to train word HMMs of allophone sequences and that not less performance than word unit HMM is obtained.

• Proceedings of the IEEK Conference
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• summer
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• pp.259-262
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• 2004

In most of the character recognition systems, the method of template matching or statistical method using hidden Markov model is used to extract and recognize feature shapes. In this paper, we used modified chain-code which has 8-directions but 4-codes, and made the chain-code of hand-written character, after that, converted it into transition chain-code by applying to HMM(Hidden Markov Model). The transition chain code by HMM is analyzed as signal flow graph by Mason's theory which is generally used to calculate forward gain at automatic control system. If the specific forward gain and feedback gain is properly set, the forward gain of transition chain-code using Mason's theory can be distinguished depending on each object for recognition. This data of the gain is reorganized as tree structure, hence making it possible to distinguish different hand-written characters. With this method, $91\%$ recognition rate was acquired.