• Journal of Astronomy and Space Sciences
• /
• v.24 no.2
• /
• pp.119-124
• /
• 2007

The relation between substorm occurrences and HILDCAA events has been an issue. We have studied the association of particle injections with substorm onsets during HILDCAA intervals for the first half of year 2003. The examination of aurora images observed by IMAGE spacecraft and electron flux data measured by LANL satellites exhibits a close association of repetitive particle injections with substorm activity. We also find that HILDCAA events can occur equally frequently during slow speed solar wind streams as long as the interplanetary magnetic field exhibits Alfvenic wave feature.

• Proceedings of the Korean Society of Potoscience Conference
• /
• 2005.06a
• /
• pp.21-23
• /
• 2005

• Journal of Korea Multimedia Society
• /
• v.16 no.8
• /
• pp.905-913
• /
• 2013

The Parallel Model Compensation Using Frequency-variant: FV-PMC for noise-robust speech recognition is a method to classify the noises, which are expected to be intermixed with input speech when recognized, into several groups of noises by setting average frequency variant as a threshold value; and to recognize the noises depending on the classified groups. This demonstrates the excellent performance considering noisy speech categorized as good using the standard threshold value. However, it also holds a problem to decrease the average speech recognition rate with regard to unclassified noisy speech, for it conducts the process of speech recognition, combined with noiseless model as in the existing PMC. To solve this problem, this paper suggests a enhanced method of recognition to prevent the unclassified through improving the extent of rating scales with use of maximum likelihood so that the noise groups, including input noisy speech, can be classified into more specific groups, which leads to improvement of the recognition rate. The findings from recognition experiments using Aurora 2.0 database showed the improved results compared with those from the method of the previous FV-PMC.

• The Journal of the Acoustical Society of Korea
• /
• v.24 no.1
• /
• pp.58-68
• /
• 2005

In this paper, we propose a scheme to improve discriminative property in the feature compensation method for robust speech recognition under noisy environments. The estimation of noisy speech model used in existing feature compensation methods do not guarantee the computation of posterior probabilities which discriminate reliably among the Gaussian components. Estimation of Posterior probabilities is a crucial step in determining the discriminative factor of the Gaussian models, which in turn determines the intelligibility of the restored speech signals. The proposed scheme employs minimum classification error (MCE) training for estimating the parameters of the noisy speech model. For applying the MCE training, we propose to identify and determine the 'competing components' that are expected to affect the discriminative ability. The proposed method is applied to feature compensation based on parallel combined mixture model (PCMM). The performance is examined over Aurora 2.0 database and over the speech recorded inside a car during real driving conditions. The experimental results show improved recognition performance in both simulated environments and real-life conditions. The result verifies the effectiveness of the proposed scheme for increasing the performance of robust speech recognition systems.

• The Journal of the Acoustical Society of Korea
• /
• v.23 no.6
• /
• pp.473-480
• /
• 2004

In this paper we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the‘data-driven’method to PCMM tot move reliable model combination and introduce a frame-synched version for estimation of environments posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance is examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

• Korea Information Processing Society Review
• /
• v.15 no.2
• /
• pp.15-28
• /
• 2008

The Korea UK e-Science Collaboration project has been aimed at supporting research and collaboration between Korean and UK researchers. Its goal is to benefit the nation by reducing cost and time in constructing the National e-Science Research Environment' by studying and benchmarking the cases in countries that have already procured advanced technologies in the area. Two joint workshops were held in a year where researchers from the two countries had the opportunities to share their research results with each other. Also, the project has supported exchanges of researchers fostering expertise in the field. In the course of the project, the e-Science Centre in the UK and KISTI have signed MoU(Memorandum of Understanding) in 2006. Moreover, there have been active research collaboration between Korea and the UK. The University of Southampton will share the BioSimGrid data with the Korean counterpart, and the University of York has provided the AURA software. In the future, KISTI and the UK NGS(National Grid Service) will organize a working group at OGF that will work mainly on the standardization of Parameter Sweep and bring it to lead the global standard. KISTI will include its own AURORA system into OMI-UK software stack, which will enable access to NGS resources through AURORA user application. The collaboration with the UK has opened up more opportunities for collaboration with other countries as well. KISTI and HLRS in Germany have agreed to share the COVISE and will have research exchanges. As such, it is expected that Korea will play a major role in e-Science research by building strategic and systematic collaborative relations with its International partners.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.5
• /
• pp.543-548
• /
• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.

• Journal of Korea Multimedia Society
• /
• v.11 no.10
• /
• pp.1325-1337
• /
• 2008

The difference of environments between training and recognition is the major reason of degradation of speech recognition. To solve this mismatch of environments, various noise processing methods have been studied. Among them, ERN(log-Energy dynamic Range Normalization) and SEN(Silence Energy Normalization) for normalization of log energy features show better performance than others. However, these methods have a problem that they can hardly achieve normalization for the relatively higher values of log energy features and the environmental mismatch caused by this problem becomes bigger especially in low SNR environments. To solve these problems, we propose applying ARMA filter as post-processing for smoothing log energy features by calculating the moving average in auto-regression scheme. From the recognition results conducted on Aurora 2.0 DB, the proposed method shows improved recognition results comparing with conventional methods.

• The Journal of the Acoustical Society of Korea
• /
• v.34 no.4
• /
• pp.316-320
• /
• 2015

In this paper, the pole filtering concept is applied to the Mel-frequency cepstral coefficient (MFCC) feature vectors in the conventional cepstral mean normalization (CMN) and cepstral mean and variance normalization (CMVN) frameworks. Additionally, performance of the cepstral mean and scale normalization (CMSN), which uses scale normalization instead of variance normalization, is evaluated in speech recognition experiments in noisy environments. Because CMN and CMVN are usually performed on a per-utterance basis, in case of short utterance, they have a problem that reliable estimation of the mean and variance is not guaranteed. However, by applying the pole filtering and scale normalization techniques to the feature normalization process, this problem can be relieved. Experimental results using Aurora 2 database (DB) show that feature normalization method combining the pole-filtering and scale normalization yields the best improvements.

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