| 논문내용 |
In this research, we used Support Vector Machines(SVMs) as the learning and recognition unit of the speech, one of artificial neural network, to segmented 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). We plan to construct a optimum hybrid classifier of SVMs and GMMs, and apply to continuous speech recognition. |
자료실 > 논문지 
발간년도 : [2014]
