• 대한음성학회지:말소리
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• 제62호
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• pp.97-112
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• 2007

The speech recognizer in general operates in noisy acoustical environments. Many research works have been done to cope with the acoustical variations. Among them, the multiple-HMM model approach seems to be quite effective compared with the conventional methods. In this paper, we consider a multiple-model approach combined with the model compensation method and investigate the necessary number of the HMM model sets through noisy speech recognition experiments. By using the data-driven Jacobian adaptation for the model compensation, the multiple-model approach with only a few model sets for each noise type could achieve comparable results with the re-training method.

• 한국전자통신학회논문지
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• 제7권4호
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• pp.855-859
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• 2012

본 연구에서는 선형시변 발진기 위상잡음 이론에 있어서 전력 스펙트럼 밀도식의 일반화된 형태를 유도한다. 유도된 전력 스펙트럼 밀도식을 바탕으로 선형시변 발진기 위상잡음 이론은 발진 신호의 전력 보존성을 예측할 수 있음을 본 연구에서 증명한다. 게다가, 유도된 전력 스펙트럼 밀도식은 선형시변 발진기 위상잡음 이론이 기본 주파수와 그 하모닉을 포함하는 전 주파수 영역에 걸친 전력 스펙트럼의 특성을 설명 할 수 있게 한다.

• 한국통신학회논문지
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• 제22권6호
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• pp.1160-1169
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• 1997

This paper presents an algorithm to separate vowels from consonants in Korean characters captured in noisy environment andto recognize them. The algorithm has been originally developed for recognition of the usage code (which is represented by a single Korean character) in the license plates of Korean vehicles. It, however, could be easily adopted to other applications with minor changes, in which character recognition is needed and the environment is noisy. The key ideas of the algorithm are to localize the vowels utilizing Hough transformation and to separate the vowels from consonants utilizing mathematical morphology. We observed that the presented algorithm effectively separates vowels even if the vowels and consonants are joined together after thresholding. We also observed that our algorithm outperforms some conventional algorithms especially when the input images are noisy. The details of the comparison study are presented in the paper.

• 대한음성학회지:말소리
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• 제57호
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• pp.153-164
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• 2006

The MMSE-STSA based speech enhancement algorithm is widely used as a preprocessing for noise robust speech recognition. It weighs the gain of each spectral bin of the noisy speech using the estimate of noise and signal power spectrum. In this paper, we investigate the influence of parameters used to estimate the speech signal and noise power in MMSE-STSA upon the recognition performance of noisy speech. For experiments, we use the Aurora2 DB which contains noisy speech with subway, babble, car, and exhibition noises. The HTK-based continuous HMM system is constructed for recognition experiments. Experimental results are presented and discussed with our findings.

• 한국정보통신학회논문지
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• 제5권3호
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• pp.443-449
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• 2001

잡음환경에서의 음성인식은 실제의 환경에서의 음성인식에서 매우 중요한 애로기술로써 이를 해결하기 위한 연구는 꾸준히 연구되고 있다. 따라서 본 연구는 음성인식분야에서 가장 많이 사용하고 있는 HMM처리 시잡음처리의 문제점을 주파수 가중치 부가 HMM으로 해결하는 방법을 제안하고 그 성능을 인식실험을 통하여 검토하였다. 그 결과 SS처리를 함께 사용하는 $MCE-\mu$, MCE-$\rho$가 가장 잡음에 강한 방식임을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1787-1788
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• 2006

In this paper, a noisy passive telemetry sensor system using Unscented Kalman Filter (UKF) is proposed. To overcome these trouble problems such as a power limitation and a estimation complexity that the general passive telemetry sensor system including IC chip has, the principle of inductive coupling was applied to the modelling of a passive telemetry sensor system (PTSS) and its noisy capacitive parameter was estimated by the UKF algorithm. Specialty, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization

• 음성과학
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• 제13권1호
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• pp.129-139
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• 2006

In this paper, we propose two effective energy feature normalization methods for robust speech recognition in noisy environments. In the first method, we estimate the noise energy and remove it from the noisy speech energy. In the second method, we propose a modified algorithm for the Log-energy Dynamic Range Normalization (ERN) method. In the ERN method, the log energy of the training data in a clean environment is transformed into the log energy in noisy environments. If the minimum log energy of the test data is outside of a pre-defined range, the log energy of the test data is also transformed. Since the ERN method has several weaknesses, we propose a modified transform scheme designed to reduce the residual mismatch that it produces. In the evaluation conducted on the Aurora2.0 database, we obtained a significant performance improvement.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.33-36
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• 2007

In this paper, we investigate a fast speaker adaptation method based on eigenvoice in several noisy environments. In order to overcome its weakness against noise, we propose a noisy environment clustering method which divides the noisy adaptation utterances into utterance groups with similar environments by the vector quantization based clustering using a cepstral mean as a feature vector. Then each utterance group is used for adaptation to make an environment dependent model. According to our experiment, we obtained 19-37 % relative improvement in error rate compared with the simultaneous speaker adaptation and environmental compensation method

• The Journal of the Acoustical Society of Korea
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• 제27권1E호
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• pp.1-10
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• 2008

A noise suppression algorithm is proposed for nonstationary noisy environments. The proposed algorithm is different from the conventional approaches such as the spectral subtraction algorithm and the minimum statistics noise estimation algorithm in that it classifies speech and noise signals in time-frequency bins. It calculates the ratio of the variance of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. If the ratio is greater than an adaptive threshold, speech is considered to be present. Our adaptive algorithm tracks the threshold and controls the trade-off between residual noise and distortion. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of simplicity and light computational load for estimating the noise. This algorithm reduces the residual noise significantly, and is superior to the conventional methods.

• 대한전자공학회논문지
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• 제21권1호
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• pp.71-78
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• 1984

본 논문에서는 ADPCM 시스템의 noisy channel에서의 성능 개선을 위한 새로운 방법을 제시하였다. 이 방법은 robust quantiser를 사용하면서 주기적으로 maximum step sixte를 수신측에 보내준다. 또한 수신측 버퍼에서는 MSB 에러검출·수정을 행한다. Noisy channel 상태에서 실제의 음성에 대해 컴퓨터 시뮬레이션한 결과 제안된 시스템의 성능은 원래의 ADPCM의 성능보다 크게 향상되었다.