• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.84-89
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• 2002

In this paper, we present the design method of noise reduction circuit in telephonic system. The circuit consists of compressor, expander and a filter. The basic idea of a proposed method compresses the audible signal in order to mask the channel noise during transmission and then expand at the reverse rate the transmitted signal to naturally recover the original signal. Of course, there should be no distortion or other degradation of the audio itself in passing through companding(compress/expand) cycle. In the compressing process, the gain of compressor is automatically controlled by the envelope level of input signal in order to increase the effective dynamic range of input signal and to improve the signal to noise ratio. The compressed rate is the root time of a audible signal. The compressed signal should be expanded at the square time of the signal to recover a original signal. Simulation shows the proposed method improves the performance of the noise reduction of a channel noise as well as stability. 

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.511-512
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• 2008

EPLA 패킷 스케줄링 알고리즘은 IEEE 802.22 WRAN 시스템의 실시간 트래픽 전송 지원을 위한 패킷 스케줄링 알고리즘으로 참고문헌[4]으로 제안되었다. 패킷 기반 무선 전송 시스템에서 실시간 트래픽의 경우 짧은 데이터 유효 시간을 가지며, 만약 데이터 유효 시간이 초과할 경우 실시간 트래픽 데이터로써의 가치를 상실하기 때문에 시스템에서는 해당패킷을 전송하지 않고 제거해 버린다. 그러므로 실시간 트래픽의 중요한 서비스 품질(QoS) 인자인 요구된 패킷 손실율을 보장하기 위해서는 실시간 트래픽의 데이터 유효 시간을 고려하여 자원을 할당하여야 한다. 기존의 패킷 스케쥴링 알고리즘들은 많은 경우 큐의 맨 앞에 위치한 패킷의 지연 시간을 고려하지만 EPLA는 패킷이 저장되는 큐 내의 다음 프레임에서 제거 될 것으로 예상되는패킷의 손실양을 고려하여 자인을 할당함으로 기존의 실시간 패킷 스케줄링 알고리즘에 비해 훨씬 좋은 성능을 보인다. 하지만 EPLA는 예상되는 패킷 손실양을 계간하기 위해서 모든 사용자의 큐에 저장된 패킷들을 확인해야하므로 높은 복잡도를 가지는 문제점이 있다. 본 논문에서는 각 사용자로부터 피드백 받은 부채널의 상태 정보를 기반으로 사용자 큐를 확인하여 횟수를 제한함으로써 패킷 손실을 성능의 손실 없이 복잡도를 줄이는 간단한 방법을 제안하고, 실시간 트래픽인 음성 트래픽과 비디오 트래픽에 대한 시뮬레이션 결과를 통해 이를 확인한다.

• The Journal of the Acoustical Society of Korea
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• v.32 no.3
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• pp.252-261
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• 2013

The common speech recognition system displays higher recognition performance in a quiet environment, while its performance declines sharply in a real environment where there are noises. To implement a speech recognizer that is robust in different speech settings, this study suggests the method of Parallel Model Combination adaptation using frequency-variant based on environment-awareness (FV-PMC), which uses variants in frequency; acquires the environmental data for speech recognition; applies it to upgrading the speech recognition model; and promotes its performance enhancement. This FV-PMC performs the speech recognition with the recognition model which is generated as followings: i) calculating the average frequency variant in advance among the readily-classified noise groups and setting it as a threshold value; ii) recalculating the frequency variant among noise groups when speech with unknown noises are input; iii) regarding the speech higher than the threshold value of the relevant group as the speech including the noise of its group; and iv) using the speech that includes this noise group. When noises were classified with the proposed FV-PMC, the average accuracy of classification was 56%, and the results from the speech recognition experiments showed the average recognition rate of Set A was 79.05%, the rate of Set B 79.43%m, and the rate of Set C 83.37% respectively. The grand mean of recognition rate was 80.62%, which demonstrates 5.69% more improved effects than the recognition rate of 74.93% of the existing Parallel Model Combination with a clear model, meaning that the proposed method is effective.

• Journal of Korean Society of Transportation
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• v.1 no.1
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• pp.43-47
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• 1983

Within the conventional transportation planning process, "trip distribution" has a significant role to play. The most widely applied trip distribution model is the gravity model, for which Wilson provided the theoretical basis in 1967. The concept of the gravity model, however, still remains ambiguous if we analyze the "trip distribution" with a disaggregate data set. Thus, this paper hypothesizes that the gravity technique is still valid even with the disaggregate data set, by proving that the estimated coefficients of the gravity model, which is derived under the principle of entropy maximization, are identical with those of the multinomial logit model, which is derived under the principle of individual utility maximization.tility maximization.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.81-86
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• 1998

트리 기반 모델링 기법 중 하나인 CART 방법을 이용하여, 운율구 추출과 운율구 사이의 휴지 기간을 모델링 하고자 한다. 모델링을 위한 특징 변수들의 유효성을 실험에 앞서 알아본 후, 생성된 트리들을 해석함으로써 제안하는 특징 변수들이 효과적임을 보인다. 음성 정보를 제외한 문서 정보만을 이용하여 실험한 결과, 운율구 경계 결정 오류율은 14.46% 이었고, 휴지 기간 예측 RMSE 가 132.61 msec 이었다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.685-687
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• 2002

일반적으로 인간이 사용하는 몇 개의 주요단어를 이용하여, 문서의 분야나 주제어가 되는 일본어 키워드를 추출하는 점에 주목한다. 먼저, 학술논문에서 저자 자신이 부여한 키워드 중 분야 명이나 주제어가 문서 중에 출현하지 않는 경우를 분석하고, 단어의 개념정보를 기초로 복합어 생성규칙을 구축한다. 문서 의미와 상관없는 키워드의 추출을 억제하기 위해 중요도 결정법을 새롭게 제안한다. 추출된 키워드의 타당성 검사를 위해 자연.음성언어에 관한 일본어 논문 65파일의 타이틀과 초록부분을 이용하여 추출된 키워드의 타당성에 대한 실험을 한 결과 추출 정밀도는 중요도의 상위 1개를 출력한 경우 75%가 되어 제안방법의 유효성을 확인할 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.16-23
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• 2001

In this paper, we propose the pseudo n-gram language models for speech recognition with middle size vocabulary compared to large vocabulary speech recognition using the statistical n-gram language models. The proposed method is that it is very simple method, which has the standard structure of ARPA and set the word probability arbitrary. The first, the 1-gram sets the word occurrence probability 1 (log likelihood is 0.0). The second, the 2-gram also sets the word occurrence probability 1, which can only connect the word start symbol and WORD, WORD and the word end symbol . Finally, the 3-gram also sets the ward occurrence probability 1, which can only connect the word start symbol , WORD and the word end symbol . To verify the effectiveness of the proposed method, the word recognition experiments are carried out. The preliminary experimental results (off-line) show that the word accuracy has average 97.7% for 452 words uttered by 3 male speakers. The on-line word recognition results show that the word accuracy has average 92.5% for 20 words uttered by 20 male speakers about stock name of 1,500 words. Through experiments, we have verified the effectiveness of the pseudo n-gram language modes for speech recognition.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.55-62
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• 2001

In this paper, we propose a new adaptative pruning algorithm which effectively reduces the search space during the recognition process. As maximum probabilities between neighbor frames are highly interrelated, an efficient pruning threshold value can be obtained from the maximum probabilities of previous frames. The main idea is to update threshold at the present frame by a combination of previous maximum probability and hypotheses probabilities. As present threshold is obtained in on-going recognition process, the algorithm does not need any pre-experiments to find threshold values even when recognition tasks are changed. In addition, the adaptively selected threshold allows an improvement of recognition speed under different environments. The proposed algorithm has been applied to a Korean Address recognition system. Experimental results show that the proposed algorithm reduces the search space of average 14.4% and 9.14% respectively while preserving the recognition accuracy, compared to the previous method of using fixed pruning threshold values and variable pruning threshold values.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1221-1228
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• 2011

The speech recognition system works well in general indoor environment. However, the recognition performance is dramatically decreased when the system is used in the real environment because of the several noises. In this paper we proposed CSFN-CMVN to improve the recognition performance of the existing CSFN(Cepstral distance based SFN). The CSFN-CMVN method is a combined method of cepstral normalization with CSFN that normalizes silence features using cepstral euclidean distance to classify speech/silence for better performance. From the test results using Aurora 2.0 DB, we could find out that our proposed CSFN-CMVN improves about 7% of more average word accuracy in all the test sets comparing with the typical silence features normalization SFN-I. We can also get improved accuracy of 6% and 5% respectively in compared tests with the conventional SFN-II and CSFN, showing the effectiveness of our proposed method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.2 s.29
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• pp.161-167
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• 2007

The goal of this paper is to develop the speech recognition system which can control the ship's auto pilot. The feature parameters predicting the speaker's intention was extracted from the sample wheel orders written in SMCP(IMO Standard Marine Communication Phrases). And we designed the post-recognition procedure based on the parameters which could make a final decision from the list of candidate words. To evaluate the effectiveness of these parameters and the procedure, the basic experiment was conducted with total 525 wheel orders. From the experimental results, the proposed pattern recognition procedure has enhanced about 42.3% over the pre-recognition procedure.