• Title, Summary, Keyword: HMM

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Reduction of Number of Free Parameters in Segmental-feature HMM (분절 특징 HMM의 매개 변수 수의 감소에 관한 연구)

  • 윤영선;오영환
    • The Journal of the Acoustical Society of Korea
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    • v.19 no.7
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    • pp.48-52
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    • 2000
  • 음성 인식에 많이 사용되는 HMM (hidden Markov model)을 개선하기 위하여 분절 특징을 사용한 분절 특징 HMM은 성능이 우수하다고 발표되었다. 그러나, 분절 길이가 증가하고 회귀 차수가 놓아질수록 분절 특징 HMM을 표현하는 매개 변수의 수도 같이 증가된다. 따라서, 본 연구에서는 상태에서 관측 가능한 분절의 분산을 분절 내의 모든 프레임에 대하여 공통적으로 표현하는 고정 분산 방법을 통하여 성능의 저하 없이 매개 변수의 수를 줄이도록 시도하였다. 실험 결과, 두 혼합 밀도인 경우 고정 분산을 이용한 분절 특징 HMM의 성능과 시변 분산을 이용한 성능의 차이가 거의 없어, 제안된 방법의 유효성을 입증하였다.

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Hidden Markov Model for Gesture Recognition (제스처 인식을 위한 은닉 마르코프 모델)

  • Park, Hye-Sun;Kim, Eun-Yi;Kim, Hang-Joon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.1
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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.

A Study on Modeling of Fighter Pilots Using a dPCA-HMM (dPCA-HMM을 이용한 전투기 조종사 모델링 연구)

  • Choi, Yerim;Jeon, Sungwook;Park, Jonghun;Shin, Dongmin
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.43 no.1
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    • pp.23-32
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    • 2015
  • Modeling of fighter pilots, which is a fundamental technology for war games using defense M&S (Modeling & Simulation) becomes one of the prominent research issues as the importance of defense M&S increases. Especially, the recent accumulation of combat logs makes it possible to adopt statistical learning methods to pilot modeling, and an HMM (Hidden Markov Model) which is able to utilize the sequential characteristic of combat logs is suitable for the modeling. However, since an HMM works only by using one type of features, discrete or continuous, to apply an HMM to heterogeneous features, type integration is required. Therefore, we propose a dPCA-HMM method, where dPCA (Discrete Principal Component Analysis) is combined with an HMM for the type integration. From experiments conducted on combat logs acquired from a simulator furnished by agency for defense development, the performance of the proposed model is evaluated and was satisfactory.

Estimation of Critical Threshold for Rejection in HMM Based Recognition Systems (HMM 기반의 인식시스템에서의 거절기능 수행을 위한 임계 문턱값 추정)

  • 김인철;진성일
    • The Journal of the Acoustical Society of Korea
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    • v.19 no.2
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    • pp.90-94
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    • 2000
  • In this paper, we propose an efficient method of estimating a critical threshold which is used to reject unreliable patterns in a HMM based recognition system. The rejection methods based on the anti-models which are formulated as the statistical hypothesis determine whether or not to accept an input pattern by comparing the likelihood ratio of HMM and anti-models to a critical threshold. It is quite difficult to fix a threshold for the probability of a HMM because the range of such probabilities varies severely depending on the chosen class model. We estimate the critical threshold, which is very class-dependent, using the likelihood scores for the training database. In our experiments, we applied the proposed estimating method of the threshold to the HMM based 3D hand gesture recognition system. We found that this method can be used successfully for rejecting unreliable input gestures regardless of the types of anti-models.

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HMM Topology Optimization using HBIC and BIC_Anti Criteria (HBIC와 BIC_Anti 기준을 이용한 HMM 구조의 최적화)

  • 박미나;하진영
    • Journal of KIISE:Software and Applications
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    • v.30 no.9
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    • pp.867-875
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    • 2003
  • This paper concerns continuous density HMM topology optimization. There have been several researches for HMM topology optimization. BIC (Bayesian Information Criterion) is one of the well known optimization criteria, which assumes statistically well behaved homogeneous model parameters. HMMs, however, are composed of several different kind of parameters to accommodate complex topology, thus BIC's assumption does not hold true for HMMs. Even though BIC reduced the total number of parameters of HMMs, it could not improve the recognition rates. In this paper, we proposed two new model selection criteria, HBIC (HMM-oriented BIC) and BIC_Anti. The former is proposed to improve BIC by estimating model priors separately. The latter is to combine BIC and anti-likelihood to accelerate discrimination power of HMMs. We performed some comparative research on couple of model selection criteria for online handwriting data recognition. We got better recognition results with fewer number of parameters.

Effective Recognition of Velopharyngeal Insufficiency (VPI) Patient's Speech Using DNN-HMM-based System (DNN-HMM 기반 시스템을 이용한 효과적인 구개인두부전증 환자 음성 인식)

  • Yoon, Ki-mu;Kim, Wooil
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.1
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    • pp.33-38
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    • 2019
  • This paper proposes an effective recognition method of VPI patient's speech employing DNN-HMM-based speech recognition system, and evaluates the recognition performance compared to GMM-HMM-based system. The proposed method employs speaker adaptation technique to improve VPI speech recognition. This paper proposes to use simulated VPI speech for generating a prior model for speaker adaptation and selective learning of weight matrices of DNN, in order to effectively utilize the small size of VPI speech for model adaptation. We also apply Linear Input Network (LIN) based model adaptation technique for the DNN model. The proposed speaker adaptation method brings 2.35% improvement in average accuracy compared to GMM-HMM based ASR system. The experimental results demonstrate that the proposed DNN-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM system.

Isolated Word Recognition Using Allophone Unit Hidden Markov Model (변이음 HMM을 이용한 고립단어 인식)

  • Lee, Gang-Sung;Kim, Soon-Hyob
    • 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.

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An Efficient Voice Activity Detection Method using Bi-Level HMM (Bi-Level HMM을 이용한 효율적인 음성구간 검출 방법)

  • Jang, Guang-Woo;Jeong, Mun-Ho
    • The Journal of the Korea institute of electronic communication sciences
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    • v.10 no.8
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    • pp.901-906
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    • 2015
  • We presented a method for Vad(Voice Activity Detection) using Bi-level HMM. Conventional methods need to do an additional post processing or set rule-based delayed frames. To cope with the problem, we applied to VAD a Bi-level HMM that has an inserted state layer into a typical HMM. And we used posterior ratio of voice states to detect voice period. Considering MFCCs(: Mel-Frequency Cepstral Coefficients) as observation vectors, we performed some experiments with voice data of different SNRs and achieved satisfactory results compared with well-known methods.

The Application of an HMM-based Clustering Method to Speaker Independent Word Recognition (HMM을 기본으로한 집단화 방법의 불특정화자 단어 인식에 응용)

  • Lim, H.;Park, S.-Y.;Park, M.-W.
    • The Journal of the Acoustical Society of Korea
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    • v.14 no.5
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    • pp.5-10
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    • 1995
  • In this paper we present a clustering procedure based on the use of HMM in order to get multiple statistical models which can well absorb the variants of each speaker with different ways of saying words. The HMM-clustered models obtained from the developed technique are applied to the speaker independent isolated word recognition. The HMM clustering method splits off all observation sequences with poor likelihood scores which fall below threshold from the training set and create a new model out of the observation sequences in the new cluster. Clustering is iterated by classifying each observation sequence as belonging to the cluster whose model has the maximum likelihood score. If any clutter has changed from the previous iteration the model in that cluster is reestimated by using the Baum-Welch reestimation procedure. Therefore, this method is more efficient than the conventional template-based clustering technique due to the integration capability of the clustering procedure and the parameter estimation. Experimental data show that the HMM-based clustering procedure leads to $1.43\%$ performance improvements over the conventional template-based clustering method and $2.08\%$ improvements over the single HMM method for the case of recognition of the isolated korean digits.

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An Empiricl Study on the Learnign of HMM-Net Classifiers Using ML/MMSE Method (ML/MMSE를 이용한 HMM-Net 분류기의 학습에 대한 실험적 고찰)

  • Kim, Sang-Woon;Shin, Seong-Hyo
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.6
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    • pp.44-51
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    • 1999
  • The HMM-Net is a neural network architecture that implements the computation of output probabilities of a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria of maximum likehood(ML) and minimization of mean squared error(MMSE) are used for learning HMM-Net classifiers. The criterion MMSE is better than ML when initial learning condition is well established. However Ml is more useful one when the condition is incomplete[3]. Therefore we propose an efficient learning method of HMM-Net classifiers using a hybrid criterion(ML/MMSE). In the method, we begin a learning with ML in order to get a stable start-point. After then, we continue the learning with MMSE to search an optimal or near-optimal solution. Experimental results for the isolated numeric digits from /0/ to /9/, a training and testing time-series pattern set, show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

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