• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1995.06a
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• pp.656-659
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• 1995

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.4
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• pp.403-410
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• 2000

In this paper, the development of a stock information retrieval system using speech recognition and its features are described. The system is based on DHMM (discrete hidden Markov model) and PLUs (phonelike units) are used as the basic unit for recognition. End-point detection and echo cancellation are included to facilitate speech input. Continuous speech recognizer is implemented to allow multi-word speech. Data collected over several months are analyzed.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1531-1540
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• 1997

In this paper, we presents automatic Korean continuous speech recognizer which is improved by the speech rate estimation and the compensation methods. Automatic continuous speech recognition is significantly more difficult than isolated word recognition because of coarticulatory effects and variations in speech rate. In order to recognize continuous speech, modeling methods of coarticulatory effects and variations in speech rate are needed. In this paper, the speech rate is measured by change of format, and the compensation is peformed by extracting relatively many feature vectors in fast speech. Coarticulatory effects are modeled by defining 514 Korean diphone set, and ETRI's 445 word DB is used for training speech material. With combining above methods, we implement automatic Korean continuous speech recognizer, which shows improved recognition rate, based on DHMM(Discrete Hidden Markov Model).

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.11-17
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• 1999

This paper proposes an observation probability smoothing technique for the robustness of a discrete hidden Markov(DHMM) model based speech recognizer. Also, an appropriate noise robust processing in car environment is suggested from experimental results. The noisy speech is often mislabeled during the vector quantization process. To reduce the effects of such mislabelings, the proposed technique increases the observation probability of similar codewords. For the noise robust processing in car environment, the liftering on the distance measure of feature vectors, the high pass filtering, and the spectral subtraction methods are examined. Recognition experiments on the 14-isolated words consists of the Korean digits and command words were performed. The database was recorded in a stopping car and a running car environments. The recognition rates of the baseline recognizer were 97.4% in a stopping situation and 59.1% in a running situation. Using the proposed observation probability smoothing technique, the liftering, the high pass filtering, and the spectral subtraction the recognition rates were enhanced to 98.3% in a stopping situation and to 88.6% in a running situation.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.299-307
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• 2003

There have been many studies on speaker normalization which aims to minimize the effects of speaker's vocal tract length on the recognition performance of the speaker independent speech recognition system. In this paper, we propose a simple vector quantizer based linear warping speaker normalization method based on the observation that the vector quantizer can be successfully used for speaker verification. For this purpose, we firstly generate an optimal codebook which will be used as the basis of the speaker normalization, and then the warping factor of the unknown speaker will be extracted by comparing the feature vectors and the codebook. Finally, the extracted warping factor is used to linearly warp the Mel scale filter bank adopted in the course of MFCC calculation. To test the performance of the proposed method, a series of recognition experiments are conducted on discrete HMM with thirteen mono-syllabic Korean number utterances. The results showed that about 29% of word error rate can be reduced, and that the proposed warping factor extraction method is useful due to its simplicity compared to other line search warping methods.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.56-60
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• 1999

본 논문은, 자동차에서 발생할 수 있는 다양한 형태의 잡음이 섞인 음성을 대상으로, 잡음에 강인한 파라미터들을 사용하여 인식기들을 구축하였으며, 이들 파라미터를 비교 평가하였다. 실험에 사용된 음성 데이터는 차종, 속도, 도로 환경, 라디오 ON/OFF, 창문 개폐여부 등 다양한 잡음 환경에서 수집하였다. 실험에서 비교된 파라미터는 MFCC(Mel-Blrequency Cepstral Coefficient)와 PLP(Perceptually Linear Prediction) 이며, 각각의 파라미터에 대해서 MKM(Modified k-mean)을 이용하여 코드북을 작성하였고, DHMM(Discrete Hidden Markov Model)을 인식알고리즘으로 사용하였다. 실험 결과로서, 아스팔트 도로에서 창문을 닫고, 라디오를 켜지 않은 상태에서 60km/h로 주행시 $96.25\%$로 가장 높은 인식률을 얻었고, 고속도로에서 창문을 열고 100km/h로 주행시에는$60\%$로 가장 낮은 인식률을 얻었다.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.66-75
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• 1998

최근 음성인식을 위한 대표적인 방법으로써 은닉 마르코프 모델이 사용되고 있으며, 이러한 방법은 음성의 특성을 잘 표현하도록 하는 음향적인 모델링 방법에 따라서 성능이 좌우된다. 본 논문에서는 상태에서의 출력확률은 견고히 추정하기 위한 방법으로 상태에서 의 출력활률을 소스들의 분포와 그들의 빈도로 가중한 출력분포로 표시하는 상태 의존 소스 양자화 모델링 방법을 제안한다. 이 방법은 한 상태 내에서 특징 파라미터들이 유사한 특성 을 가지며, 그들의 변이가 다른 상태에 있는 특징 파라미터들에 비해서 작다는 사실에 기반 한다. 실험결과에 의하면, 제안된 방법이 기존의 baseline시스템보다 단어 인식율의 경우는 2.7%, 문장 인식율의 경우 3.6%의 향상을 보였다. 이러한 결과로부터 제안된 SDSQ-DHMM이 인식율 향상면에서 유효하며, HMM에 있어서 상태별 출력확률의 견고한 추정을 위한 대안으로 사용될 수 있을 것으로 판단된다.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.675-678
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• 1999

본 논문에서는 음소별 코드북 개수의 선택과 벡터 양자화에 따른 음소 인식률과 고립단어 인식률에 대하여 다룬다. 음성모델은 이산 확률 밀도를 갖는 DHMM(Discrete Hidden Markov Model)을 사용하였으며, 코드북 생성과 벡터 양자화 알고리즘으로는 K-means 알고리즘과 LBG(Linde, Buzo, Gray) 알고리즘을 사용하였다 음소별 코드북 개수와 벡터 양자화를 최적화함으로써 음소 인식률을 향상시킬 수 있으며, 그 결과 안정된 고립단어 인식률을 얻을 수 있다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.171-174
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• 2000

본 논문에서는 홈 오토메이션 시스템을 음성인식을 도입하여 설계하였다. 많은 계산량과 방대한 양의 데이터의 처리를 요구하는 음성인식을 DSP(Digital Signal Processor)를 통하여 구현해 보고자 본 연구를 수행하였다. 이를 위해 실시간 끝점검출기를 이용하여 추가의 입력장치가 필요하지 않도록 시스템을 구성하였다. 특징벡터로는 LPC로부터 유도한 10차의 cepstrum과 log 스케일 에너지를 이용하였고, 음소수에 따라 상태의 수를 다르게 구성한 DHMM(Discrete Hidden Marcov Model)을 인식기로 사용하였다. 인식단어는 가정 자동화를 위하여 많이 쓰일 수 있는 10개의 단어를 선택하여 화자 독립으로 인식을 수행하였다. 또한 단어가 인식이 되면 인식된 단어에 대해서 현재의 상태를 음성으로 알려주고 이에 대해 자동으로 실행하도록 시스템을 구성하였다.