SVMs을 이용한 효율적인 음소단위 분할
Efficient Segmentation by Phoneme Unit using SVMs
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초록
본 연구에서는 음성의 학습 및 인식 단위로서 연속음성을 초성, 중성, 종성의 음소단위로 분할하기 위하여 인공 신경회로망의 하나인 SVMs을 사용하였으며 분할한 음소단위의 음성으로 연속음성인식에 적용하여 그 성능을 살펴보았다. 음소경계는 단 구간에서의 최대 주파수를 가진 알고리즘에 의하여 결정되며 또한 음성인식처리는 CHMM에 의하여 이루어지며 GMM 및 목측에 의한 분할결과와도 비교하여 살펴보았다. 시뮬레이션 결과로부터 분할성능에서 전반적으로는 SVMs이 양호하며 특히 초성에서 더 우수하며 중성, 종성에서는 GMM이 보다 효율적임을 알 수 있었다. 향후 SVMs과 GMM을 결합한 하이브리드 구조의 최적 음소 분할기를 구성하고 연속 음성인식에 적용할 계획이다.
Abstract
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.
참고문헌 (9)
- J. Ghosh, "Multiclassifier systems: back to the future", Proceedings of the 3rd International Workshop on Multiple Classifier Systems, Lecture Note in Computer Science, 2364, pp.1~15 (2002)
- N. Cristianini, "An introduction to support vector machine and other kernel based learning methods" (2000), Cambridge University Press
- Bernhard Scholkopf, "Learning with Kernels" (2002), The MIT Press
- A tutorial on support vector machines for pattern recognition, "Christopher J.C. Burges", Bell Laboratories, Lucent Technologies, ~ (2008)
- Multi-class support vector machines, "J. Weston", University of London, ~ (2008)
- Yonas B. Dibike, "Model introduction with support vector machines:introduction and applications", ASCE Journal of Computing in Civil Engineering, 15(3), pp.208~216 (2007)
- Robert Freund and Federico Girosi, Support vector machines: training and applications, "Edgar E. Osuna", C.B.C.L, ~, pp.Paper, No. 144 (2007)
- Philip Clarkson, "On the use of support vector machines for phonetic classification", ICASSP (2005)
- X. Peng, "TPMSVM: A novel twin parametric margin support vector machine for pattern recognition", pattern recognition, 44(10-11), pp.2678~2692 (2011)