• Journal of the Institute of Convergence Signal Processing
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• v.10 no.3
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• pp.170-175
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• 2009

In this paper, we propose real time automatic speaker verification (ASV) system to protect personal information on personal digital assistant (PDA) device. Recently, the capacity of PDA has extended and been popular, especially for mobile environment such as mobile commerce (M-commerce). However, there still exist lots of difficulties for practical application of ASV utility to PDA device because it requires too much computational complexity. To solve this problem, we apply the method to relieve the computational burden by performing the preprocessing such as spectral subtraction and speech detection during the speech utterance. Also by applying the hidden Markov model (HMM) optimal state alignment and the sequential probability ratio test (SPRT), we can get much faster processing results. The whole system implementation is simple and compact enough to fit well with PDA device's limited memory and low CPU speed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.216-223
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• 2003

In this paper, we present SCCRS(Speech and Character Combined Recognition System) for speaker /writer independent. on-line multimodal interfaces. In general, it has been known that the CHMM(Continuous Hidden Markov Mode] ) is very useful method for speech recognition and on-line character recognition, respectively. In the proposed method, the same CHMM is applied to both speech and character recognition, so as to construct a combined system. For such a purpose, 115 CHMM having 3 states and 9 transitions are constructed using MLE(Maximum Likelihood Estimation) algorithm. Different features are extracted for speech and character recognition: MFCC(Mel Frequency Cepstrum Coefficient) Is used for speech in the preprocessing, while position parameter is utilized for cursive character At recognition step, the proposed SCCRS employs OPDP (One Pass Dynamic Programming), so as to be a practical combined recognition system. Experimental results show that the recognition rates for voice phoneme, voice word, cursive character grapheme, and cursive character word are 51.65%, 88.6%, 85.3%, and 85.6%, respectively, when not using any language models. It demonstrates the efficiency of the proposed system.

• Journal of Navigation and Port Research
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• v.43 no.4
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• pp.237-242
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• 2019

Behavioral errors of the seafarers are one of the major causes of collisions and are usually corrected through education and training. To correct this behavioral error, the structure in which the behavioral error occurs needs to be identified and analyzed. For this purpose, behavior observation data were obtained through ship maneuvering simulation for collision encounters. The 9-state behavior classification frame proposed by Reason was used for the behavior observation and 50 university students were involved in the experiment. Behavioral analysis used the behavioral model of collision avoidance success and failure, which was developed from the 9-state Left-to-Right Hidden Markov modeling technique. As a result of the experiment, the difference between behaviors of success and failure of collision avoidance was clearly identified, and the linkage between 9-state behaviors, required to prevent collision, was derived.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.94-99
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• 1999

It is the hybrid structure of HMM and neural network(NN) that shows high recognition rate in speech recognition algorithms. And it is a method which has majorities of statistical model and neural network model respectively. In this study, we propose a new style of the hybrid structure of semi-continuous HMM(SCHMM) and radial basis function(RBF), which re-estimates weighting coefficients probability affecting observation probability after Baum-Welch estimation. The proposed method takes account of the similarity of basis Auction of RBF's hidden layer and SCHMM's probability density functions so as to discriminate speech signals sensibly through the learned and estimated weighting coefficients of RBF. As simulation results show that the recognition rates of the hybrid structure SCHMM/RBF are higher than those of SCHMM in unlearned speakers' recognition experiment, the proposed method has been proved to be one which has more sensible property in recognition than SCHMM.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.703-709
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• 2019

This paper proposes speech recognition systems employing Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures combined with Hidden Markov Moldel (HMM) to effectively recognize the speech of VeloPharyngeal Insufficiency (VPI) patients, and compares the recognition performance of the systems to the Gaussian Mixture Model (GMM-HMM) and fully-connected Deep Neural Network (DNNHMM) based speech recognition systems. In this paper, the initial model is trained using normal speakers' speech and simulated VPI speech is used for generating a prior model for speaker adaptation. For VPI speaker adaptation, selected layers are trained in the CNN-HMM based model, and dropout regulatory technique is applied in the LSTM-HMM based model, showing 3.68 % improvement in recognition accuracy. The experimental results demonstrate that the proposed LSTM-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM and fully-connected DNN-HMM system.

• Journal of IKEEE
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• v.22 no.1
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• pp.162-167
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• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.21-25
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• 1999

This paper proposes methods that replace a missing triphone with a new one selected or created by existing triphones, and compares the results. The recognition system uses DMS (Dynamic Multisection) model for acoustic modeling. DMS is one of the statistical recognition techniques proper to a small - or mid - size vocabulary system, while HMM (Hidden Markov Model) is a probabilistic technique suitable for a middle or large system. Accordingly, it is reasonable to use an effective algorithm that is proper to DMS, rather than using a complicated method like a polyphone clustering technique employed in HMM-based systems. In this paper, four methods of filling missing triphones are presented. The result shows that a proposed replacing algorithm works almost as well as if all the necessary triphones existed. The experiments are performed on the 500＋ word DMS speech recognizer.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.443-446
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• 2007

음성신호는 잡음 또는 전송 채널의 특성에 의하여 왜곡되고, 왜곡된 음성은 음성인식 및 화자인식의 성능을 크게 저하시킨다. 이러한 문제점을 극복하기 위해 본 논문에서는 Gaussian mixture model (GMM)에 적용된 신호대잡음비 (SNR)기반 신뢰도 가중 기법[1][2]을 Hidden Markov model(HMM) 디코더에 변형하여 적용하였다. HMM 디코더 변형은 HMM 상태별 관측확률을 논문 [1]에서 제시된 신뢰도로 가중함으로써 이루어졌다. 제안한 방법의 성능을 확인하기 위해 ETRI에서 만든 한국어 화자인식용 휴대폰 음성 DB를 사용하여 문맥종속 화자식별 실험을 하였다. 실험결과 기존 방법에 비해 제안한 방법의 화자인식률이 크게 향상됨을 확인 할 수 있었다.

• MALSORI
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• no.43
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• pp.137-150
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• 2002

Speech segmentation at phoneme level is important for corpus-based text-to-speech synthesis. In this paper, we examine acoustic modeling methods to improve the performance of automatic speech segmentation system based on Hidden Markov Model (HMM). We compare monophone and triphone models, and evaluate several model training approaches. In addition, we employ an energy-based postprocessing scheme to make correction of frequent boundary location errors between silence and speech sounds. Experimental results show that our system provides 71.3% and 84.2% correct boundary locations given tolerance of 10 ms and 20 ms, respectively.