• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.252-255
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• 2008

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is expressed as the SVM of optimally weighted mel-frequency cepstral coefficients (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to each MFCC filter bank which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification using SVM.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• The Journal of the Acoustical Society of Korea
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• v.27 no.6
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• pp.319-324
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• 2008

In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.100-106
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• 2008

In this paper we apply a discriminative weight training employing power spectral flatness measure (PSFM) to a statistical model-based voice activity detection (VAD) in various noise environments. In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratio test (LRT) based on a minimum classification error (MCE) method which is different from the previous works in th at different weights are assigned to each frequency bin and noise environments depending on PSFM. According to the experimental results, the proposed approach is found to be effective for the statistical model-based VAD using the LRT.

• The Journal of the Acoustical Society of Korea
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• v.26 no.5
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• pp.194-198
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• 2007

In this paper, we apply a discriminative weight training to a statistical model-based voice activity detection(VAD). In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratios(LRs) based on a minimum classification error(MCE) method which is different from the previous works in that different weights are assigned to each frequency bin which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for the statistical model-based VAD using the LR test.

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.443-449
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• 2008

One of the key issues in practical speech processing is to achieve robust Voice Activity Deteciton (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ equally weighted likelihood ratios (LRs), which, however, deviates from the real observation. Furthermore voice activities in the adjacent frames have strong correlation. In other words, the current frame is highly correlated with previous frame. In this paper, we propose the effective VAD approach based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to both the likelihood ratio on the current frame and the decision statistics of the previous frame.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.138-142
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• 2009

We propose an effective voice-phishing detection algorithm based on discriminative weight training. The detection of voice phishing is performed based on a Gaussian mixture model (GMM) incorporaiting minimum classification error (MCE) technique. Actually, the MCE technique is based on log-likelihood from the decoding parameter of the SMV(Selectable Mode Vocoder) directly extracted from the decoding process in the mobile phone. According to the experimental result, the proposed approach is found to be effective for the voice phishing detection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.97-103
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• 2001

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. Although the ML method has good performance, it dose not take account into discrimination to other words. To complement this problem, a word verification method by re-recognition of the recognized word and its similar word using the discriminative function of the two words. To find the similar word, the probability of other words to the HMM is calculated and the word showing the highest probability is selected as the similar word of the mode. To achieve discrimination to each word the weight to each state is appended to the HMM parameter. The weight is calculated by genetic algorithm. The verificator complemented discrimination of each word and reduced the error occurred by similar word. As a result of verification the total error is reduced by about 22%

• Journal of Society of Preventive Korean Medicine
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• v.26 no.2
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• pp.37-53
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• 2022

Objectives : This study analyzed the concordance between student self-assessment and peer-evaluation in a role play using the Clinical Performance Examination (CPX) scenario developed based on Korean medical syndrome differentiation. Methods : The subjects of this study are first-year-students majoring in Korean Medicine. The role play based on clinical case was performed in the class of Korean Medicine Classics. Feedback on clinical skill competency got through student self-assessment and peer-evaluation, and this study was compared and analyzed of result. Results : A simple comparison of the results of self-assessment and peer-evaluation in the evaluation results of clinical skill competency may appear to be consistent. However, it was not statistically significant. It is necessary to enhance the discriminative ability in the evaluation of clinical skill competency. It will be possible to improve a bit by relatively increasing the weight of the scores on the items that students expect to respond differently among the evaluation items. In addition, in order to dramatically improve the systemicity and reliability of the evaluation of clinical skill competency itself, it is necessary to introduce the Introduction to Clinical Traditional Korean Medicine (ICTKM) course. Conclusions : Student's self-assessment and peer-evaluation as feedback on clinical skill competency are suitable for the purpose of education and training. However, the reliability of the evaluation was not statistically significant.