• Proceedings of the ESK Conference
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• 1997.04a
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• pp.107-113
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• 1997

In this paper, an interfacing method to control rehabilitation assitance system with bio-signal is proposed. Controlling with EMG signals method has certain advantage on signal-collecting, but has some drawbacks in the function resolution of EMG signals because data-processing process is not efficient. To improve function-resolution and to increase the efficiency of EMG signal interfacing with rehabilitation assistance system, Multi-layer Perception which is highly effective with static signal and hidden-Markov model for dynamic signal resolving are fused together. In proposed method. The direction and average speed of the rehabilitation assitance system are controlled by the trajectory control and estimation of the moving direction result from the fused model. From the experiment, proposed GMM and 2-level MLP hybrid-classifier yielded 8.6% perception-error rate, improving function resolution. New acceleration control method constructed with 3 nested linear filter produced continuous acceleration paths without the information of destination point. Thus, the mass output caused by non- continuous acceleration-deceleration was eliminated. In the simulation, the necessary calculation, in the case of multiplication, was reduced by 11.54%.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.25-32
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• 1995

In this paper, we propose to use a parallel-branch subunit model for improved word recognition. The model is obtained by splitting off each subunit branch based on mixture component in continuous hidden Markov model(continuous HMM). According to simulation results, the proposed model yields higher recognition rate than the single-branch subunit model or the parallel-branch subunit model proposed by Rabiner et al[1]. We show that a proper combination of the number of mixture components and the number of branches for each subunit results in increased recognition rate. To study the recognition performance of the proposed algorithms, the speech material used in this work was a vocabulary with 1036 Korean words.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.544-546
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• 2002

본 논문은 CDHMM(Continuous Density Hidden Markov Model)의 훈련하는 방법을 동적 다중 그룹 혼합 가중치(Dynamic Mutli-Group mixture weight)을 이용하여 재구성하는 방법을 제안한다. 음성은 Hidden 상태열에 의하여 특성화되고, 각 상태는 가중된 혼합 가우시안 밑도 함수에 의해 표현된다. 음성신호를 더욱더 정확하게 계산하려면 각 상태를 위한 가우시안 함수를 더욱더 많이 사용해야 하며 이것은 많은 계산량이 요구된다. 이러한 문제는 가우시안 분포 확률의 통계적인 평균을 이용하면 계산량을 줄일 수 있다. 그러나 이러한 기존의 방법들은 다양한 화자의 발화속도와 가중치의 적용이 적합하지 못하여 인식률을 저하시키는 단점을 가지고 있다. 이 문제를 다양한 화자의 발화속도에 적합하도록 화자의 화자의 발화속도에 따라 동적으로 5개의 그룹으로 구성하고 동적 다중 그룹 혼합 가중치를 적용하여 CDHMM 파라미터를 재구성함으로써 8.5%의 인식율이 증가되었다.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1034-1037
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• 2009

Sign language spotting is the task of detecting the start and end points of signs from continuous data and recognizing the detected signs in the predefined vocabulary. The difficulty with sign language spotting is that instances of signs vary in both motion and shape. Moreover, signs have variable motion in terms of both trajectory and length. Especially, variable sign lengths result in problems with spotting signs in a video sequence, because short signs involve less information and fewer changes than long signs. In this paper, we propose a method for spotting variable lengths signs based on semi-CRF (semi-Markov Conditional Random Field). We performed experiments with ASL (American Sign Language) and KSL (Korean Sign Language) dataset of continuous sign sentences to demonstrate the efficiency of the proposed method. Experimental results show that the proposed method outperforms both HMM and CRF.

• Speech Sciences
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• v.10 no.3
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• pp.251-261
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• 2003

There has been many research efforts to overcome the problems of speech recognition in noisy conditions. Among the noise-robust speech recognition methods, model-based adaptation approaches have been shown quite effective. Particularly, the PMC (parallel model combination) method is very popular and has been shown to give considerably improved recognition results compared with the conventional methods. In this paper, we experimented with the VTS (vector Taylor series) algorithm which is also based on the model parameter transformation but has not attracted much interests of the researchers in this area. To verify the effectiveness of it, we employed the algorithm in the continuous density HMM (Hidden Markov Model). We compared the performance of the VTS algorithm with the PMC method and could see that the it gave better results than the PMC method.

• 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 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 Acoustical Society of Korea Conference
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• autumn
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• pp.109-112
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• 1999

본 연구는 음소 단위의 CHMM(Continuous Hidden Markov Model)을 이용한 한국어 연속 음성인식에 관한 내용이다. 연구실 환경에서 음성으로 전화를 걸기 위하여 연속 숫자음 인식을 수행하였다. ETRI 445 데이터를 사용하여 초기의 모델은 ML(Maximum Likelihood) 추정법을 이용하여 작성하였고 적응화를 위해 최대 사후 확률 추정법을 사용하였다. 연속 숫자음의 인식을 위하여 한국어 숫자음 음성의 음향학적 특성을 고려하여 발성 사전을 작성하였고, 음절 단위로 되어있는 한국어 숫자음의 모든 경우를 고려하여 복수개의 단어를 사전에 등록하였다. 또한 숫자음의 알 뒤 연음현상을 고려하여 작성한 21 종류의 7자리 숫자음과 이를 음절 단위로 세그먼트한 숫자음을 DB로 사용하여 적응화를 수행하였다. 이의 효율성을 입증하기 위하여 ETRI에서 작성한 35종류의 4연속 숫자음 목록을 대상으로 인식실험을 수행하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.10a
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• pp.221-225
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• 1998

본 논문에서는 연속분포(Continuous) HMM(hidden Markov model)을 기반으로 하여 한국어 고립단어인식 시스템을 설계, 구현하였다. 시스템의 학습과 평가를 위해 자동차 항법용 음성 명령어 도메인에서 추출한 10개의 고립단어를 대상으로 음성 데이터 베이스를 구축하였다. 음성 특징 파라미터로는 MFCCs(Mel Frequency Cepstral Coefficients)와 차분(delta) MFCC 그리고 에너지(energy)를 사용하였다. 학습 데이터로부터 추출한 18개의 유사 음소(phoneme-like unit : PLU)를 인식단위로 HMM 모델을 만들었고 조음 결합 현상(채-articulation)을 모델링 하기 위해 트라이폰(triphone) 모델로 확장하였다. 인식기 평가는 학습에 참여한 음성 데이터와 학습에 참여하지 않은 화자가 발성한 음성 데이터를 이용해 수행하였으며 평균적으로 97.5%의 인식성능을 얻었다.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.16-20
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• 2000

Most of speech recognition systems are using Hidden Markov Model based on statistical modelling frequently. In Korean isolated telephone digit speech recognition, high recognition rate is gained by using HMM if many training data are given. But in Korean continuous telephone digit speech recognition, HMM has some limitations for similar telephone digits. In this paper we suggest a way to overcome some limitations of HMM by using discriminant learning based on minimal classification error criterion in Korean continuous telephone digit speech recognition. The experimental results show our method has high recognition rate for similar telephone digits.