• BMB Reports
• /
• 제46권6호
• /
• pp.289-294
• /
• 2013

To maintain cellular homeostasis against the demands of the extracellular environment, a precise regulation of kinases and phosphatases is essential. In cell cycle regulation mechanisms, activation of the cyclin-dependent kinase (CDK1) and cyclin B complex (CDK1:cyclin B) causes a remarkable change in protein phosphorylation. Activation of CDK1:cyclin B is regulated by two auto-amplification loops-CDK1:cyclin B activates Cdc25, its own activating phosphatase, and inhibits Wee1, its own inhibiting kinase. Recent biological evidence has revealed that the inhibition of its counteracting phosphatase activity also occurs, and it is parallel to CDK1:cyclin B activation during mitosis. Phosphatase regulation of mitotic kinases and their substrates is essential to ensure that the progression of the cell cycle is ordered. Outlining how the mutual control of kinases and phosphatases governs the localization and timing of cell division will give us a new understanding about cell cycle regulation.

• 융합신호처리학회논문지
• /
• 제15권2호
• /
• pp.37-41
• /
• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Journal of Astronomy and Space Sciences
• /
• 제21권2호
• /
• pp.101-108
• /
• 2004

The terrestrial nightglow emission in near infrared region were obtained using a Fourier Transform Spectrometer(FTS) at Esrange, Sweden ($67.90^{\circ}$N, $21.10^{\circ}$E) and the OH(4- 2) bands were used to derive temperature and airglow emission rate of the upper mesosphere. For this study, we analyzed data taken during winter of 2001/2002 and performed spectral analysis to retrieve wave information. From the Lomb-Scargle spectral analysis to the measured temperatures, dominant oscillations at various periods near tidal frequency are found. Most commonly observed waves are 4, 6, and 8 hour oscillations. Because of periods and persistence, the observed oscillations are most likely of tidal origin, i.e. zonally symmetric tides which are known to have their maximum amplitudes at the pole.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2006년도 추계학술대회 발표논문집
• /
• pp.7-10
• /
• 2006

본 논문에서는 음성의 모델을 이용하여 확률적인 기반으로 잡음의 마스킹 정도를 측정하는 방법에 대해서 제시한다. 잡음의 마스킹 정도를 측정하는 기준으로서 '잡음 마스킹 확률'을 구하는 방법에 대해서 설명하고 이의 특성에 대해서 알아본다. 그리고 잡음에 대한 '잡음 마스킹 확률'을 이용하여 잡음 환경에서의 음성인식 특징벡터의 성능 향상에 대해 적용해 보았다. 제안된 방법은 ETSI 에서 음성인식 표준실험으로 제시한 Aurora2 데이터베이스 상에서 실험해 보았다. 그 결과 기존의 알고리즘에 비해 16.58%의 성능 향상을 이루어 낼 수 있었다.

• 말소리와 음성과학
• /
• 제6권2호
• /
• pp.29-34
• /
• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 2002년도 하계학술발표대회 논문집 제21권 1호
• /
• pp.83-86
• /
• 2002

훈련과정과 인식과정에서의 주변환경 잡음과 채널 특성 등의 불일치는 음성인식 성능을 급격히 저하시킨다. 이러한 불일치를 보상하기 위해서 켑스트럼 영역에서의 다양한 전처리 방법이 시도되고 있으며 최근에는 stereo 데이터와 잡음 음성의 Gaussian Mixture Model (GMM)을 이용해 보상벡터를 구하는 SPLICE 방법이 좋은 결과를 보이고 있다(1). 기존의 SPLICE가 전체 발성에 대해서 음향학적인 정보만으로 Gaussian 모델을 구하는 반면 본 논문에서는 발성에 해당하는 음소정보를 고려하여 전체 음향 공간을 각 음소에 대해 나누어서 모델링하고 각 음소에 대한 Gaussian 모델과 그 음소에 해당하는 음성데이터만을 이용하여 음소별 보상벡터가 훈련되도록 하였다. 이 경우 보상벡터는 잡음이 각 음소에 미치는 영향을 보다 자세히 나타내게 된다. Aurora 2 데이터베이스를 이용한 실험결과, 제안된 방법이 기존의 SPLICE방법에 비해 성능향상을 보였다.

• 말소리와 음성과학
• /
• 제9권2호
• /
• pp.103-110
• /
• 2017

The pole filtering concept has been successfully applied to cepstral feature normalization techniques for noise-robust speech recognition. In this paper, it is proposed to apply the pole filtering selectively only to the speech intervals, in order to further improve the recognition performance for short utterances in noisy environments. Experimental results on AURORA 2 task with clean-condition training show that the proposed selectively pole-filtered cepstral mean normalization (SPFCMN) and selectively pole-filtered cepstral mean and variance normalization (SPFCMVN) yield error rate reduction of 38.6% and 45.8%, respectively, compared to the baseline system.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2018년도 추계학술발표대회
• /
• pp.515-516
• /
• 2018

본 논문은 의료 영상을 기반으로 중재시술을 위한 네비게이션 시스템을 제안한다. 네비게이션 시스템은 의료영상을 기반으로 로드맵을 제공하며, 병변지역까지의 최단경로를 A-start 알고리즘을 이용하여 네비게이션 서비스를 제공한다. 또한 카테터의 추적은 자기장 추적방법을 채택한 Aurora 시스템에 의해 실시간으로 모니터링 한다. 끝으로 뇌질환 팬텀을 통해 제안한 시스템의 제공하는 서비스 수행 결과를 보인다. 향후 수술 적용 범위를 넓혀 다양한 질환에 적용시키고자 한다.

• 대한음성학회지:말소리
• /
• 제54호
• /
• pp.63-72
• /
• 2005

The performance of speech recognition system is degraded by the mismatch between training and test environments. Many studies have been presented to compensate for noise components in the cepstral domain. Recently, higher order cepstral moment normalization method has been introduced to improve recognition accuracy. In this paper, we present real-time high order moment normalization method with post-processing smoothing filter to reduce the parameter estimation error in higher order moment computation. In experiments using Aurora2 database, we obtained error rate reduction of 44.7% with proposed algorithm in comparison with baseline system.

• 대한음성학회지:말소리
• /
• 제60호
• /
• pp.145-164
• /
• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.