• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.29-35
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• 2010

While diversification of the use of video in the prevalence of cheap video equipment, augmented reality can print additional real-world images and video image. Although many recent advent augmented reality techniques, currently attempting to correct the character recognition is performed. In this paper characters marked with a visual marker recognition, and the color to match the marker color of the characters finds. And, it was shown on the screen by the character recognition. In this paper, by applying the phoneme pattern segmentation algorithm by the horizontal projection, we propose to segment the phoneme to match the six types of Hangul representation. Throughout the experiment sample of phoneme segmentation using augmented reality showed proceeding result at each step, and the experimental results was found to be that detection rate was above 90%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.4-10
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• 2000

The work presented in this paper is about a postprocessor, which improves the performance of automatic speech segmentation system by correcting the phoneme boundary errors. We propose a postprocessor that reduces the range of errors in the auto labeled results that are ready to be used directly as synthesis unit. Starting from a baseline automatic segmentation system, our proposed postprocessor trains the features of hand labeled results using multi-layer perceptron(MLP) algorithm. Then, the auto labeled result combined with MLP postprocessor determines the new phoneme boundary. The details are as following. First, we select the feature sets of speech, based on the acoustic phonetic knowledge. And then we have adopted the MLP as pattern classifier because of its excellent nonlinear discrimination capability. Moreover, it is easy for MLP to reflect fully the various types of acoustic features appearing at the phoneme boundaries within a short time. At the last procedure, an appropriate feature set analyzed about each phonetic event is applied to our proposed postprocessor to compensate the phoneme boundary error. For phonetically rich sentences data, we have achieved 19.9 % improvement for the frame accuracy, comparing with the performance of plain automatic labeling system. Also, we could reduce the absolute error rate about 28.6%.