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

• Bulletin of the Korean Space Science Society
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• 2009.04a
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• pp.41.2-41.2
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• 2009

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.207-210
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• 2003

In this paper, the combinations of speech enhancement techniques are experimented. Specifically, the spectral subtraction, KLT based comb-filtering, and their combinations are applied to the Aurora2 database. The results show that recognition accuracy is improved when KLT based comb-filtering is applied after spectral subtraction.

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

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

• 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 Korea Multimedia Society
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• v.11 no.10
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• pp.1325-1337
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• 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
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• v.24 no.1
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• pp.58-68
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• 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
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• v.23 no.6
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• pp.473-480
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• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1665-1674
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• 2011

In this paper, we proposed an algorithm for speech enhancement of speeches corrupted by colored noise. When there is no correlation between colored noise and speech signal, the colored noise turns into white noise through whitening transformation. This transformed signal has been applied to the generalized subspace approach for speech enhancement. The speech spectral distortion, produced by the whitening transformation as pre-processing, has been restored by using the inverse whitening transformation as post-processing of the proposed algorithm. The performance of the proposed algorithm for speech enhancement has been confirmed by computer simulation. The colored noises used in this experiment were car noise and multi-talker babble. It is confirmed that the proposed algorithm shows better performance from SNR and SSD viewpoint over the previous approach with the data from the AURORA and TIMIT data base.