• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.1-6
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• 2000

In this paper, the hybrid system of HMM and neural network is proposed and show better recognition rate of the post-process procedure which minimizes the process error of recognition than that of HMM(Hidden Markov Model) only used. After the HMM training by training data, testing data that are not taken part in the training are sent to HMM. The output probability from HMM output by testing data is used for the training data of the neural network, post processor. After neural network training, the hybrid system is completed. This hybrid system makes the recognition rate improvement of about $4.5\%$ in MLP and about $2\%$ in RBFN and gives the solution to training time of conventional hybrid system and to decrease of the recognition rate due to the lack of training data in real-time speech recognition system.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.244-250
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• 2014

The Most Serious Engine Faults Are Those That Occur Within The Engine. Traditional Engine Fault Diagnosis Is Highly Dependent On The Engineer'S Technical Skills And Has A High Failure Rate. Neural Networks And Support Vector Machine Were Proposed For Use In A Diagnosis Model. In This Paper, Noisy Sound From Faulty Engines Was Represented By The Mel Frequency Cepstrum Coefficients, Zero Crossing Rate, Mean Square And Fundamental Frequency Features, Are Used In The Hidden Markov Model For Diagnosis. Our Experimental Results Indicate That The Proposed Method Performs The Diagnosis With A High Accuracy Rate Of About 98% For All Eight Fault Types.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.175-178
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• 2002

본 논문에서는 Hidden Markov Model(HMM)을 이용하여 corpus 기반 TTS에 사용할 DB를 자동 음소 분할 해주는 시스템을 구현하였다. HMM을 이용해서 음소 분할 할 경우 HMM을 모델링 하는 방법에 따라 많은 성능의 차이가 난다. 따라서 본 논문에서는 HMM 모델링 방법에 따른 몇 가지 실험 및 성능 평가를 하였다. 실험 결과 음성 인식과는 달리 HMM모델링 시 triphone 모델보다 monophone 모델의 성능이 더 우수하였으며, 에너지 기반의 후처리를 통해 성능 향상을 얻을 수 있었다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Annual Conference on Human and Language Technology
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• 2005.10a
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• pp.7-12
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• 2005

본 논문은 은닉 마코프 모델(hidden Markov Model: HMM)을 2 단계로 적용하여 논문 모집공고(Call-for-Paper: CFP)에서 필요한 정보를 추출하는 방법을 제안한다. HMM은 순차적인 흐름의 정보를 담고 있는 데이터를 잘 설명할 수 있으며 CFP가 담고 있는 정보에는 순서가 있기 때문에, CFP를 HMM으로 설명할 수 있다. 하지만, 문서를 전체적으로(global) 파악하는 HMM만으로는 정보의 정확한 경계를 파악할 수 없다. 따라서 첫 번째 단계로 CFP문서에서 구(phrase) 단위를 구성하는 단어의 열에 대한 HMMs을 통해 국부적으로(local) 정보의 경계와 대강의 종류를 파악한다. 그리고 두 번째 단계에서 전체적인 문서의 내용 흐름에 근거하여 구축된 HMM을 이용하여 그 정보가 세부적으로 어떤 종류의 정보인지 정한다. PASCAL challenge에서 제공받은 Cff 말뭉치에 대한 첫 번째 단계의 실험 결과, 0.60의 재현률과 0.61의 정확률을 보였으며, 정확률과 재현률을 바탕으로 F-measure를 측정한 결과 0.60이었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

This paper proposes a novel hidden Markov model (HMM)-based gesture recognition method and applies it to an HCI to control a computer game. The novelty of the proposed method is two-fold: 1) the proposed method uses a continuous streaming of human motion as the input to the HMM instead of isolated data sequences or pre-segmented sequences of data and 2) the gesture segmentation and recognition are performed simultaneously. The proposed method consists of a single HMM composed of thirteen gesture-specific HMMs that independently recognize certain gestures. It takes a continuous stream of pose symbols as an input, where a pose is composed of coordinates that indicate the face, left hand, and right hand. Whenever a new input Pose arrives, the HMM continuously updates its state probabilities, then recognizes a gesture if the probability of a distinctive state exceeds a predefined threshold. To assess the validity of the proposed method, it was applied to a real game, Quake II, and the results demonstrated that the proposed HMM could provide very useful information to enhance the discrimination between different classes and reduce the computational cost.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1958-1962
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• 2005

A driver's emotion is a very important factor of safe driving. This paper classified a driver's emotion into 3 major emotions, can be occur when driving a car: Surprise, Joy, Tired. And It evaluated the classifier using Hidden Markov Models, which have observation sequence as bio-signals. It used the 2-D emotional plane to classfiy a human's general emotion state. The 2-D emotional plane has 2 axes of pleasure-displeasure and arsual-relaxztion. The used bio-signals are Galvanic Skin Response(GSR) and Heart Rate Variability(HRV), which are easy to acquire and reliable. We classified several moving pictures into 3 major emotions to evaluate our HMM system. As a result of driving simulations for each emotional situations, we can get recognition rates of 67% for surprise, 58% for joy and 52% for tired.