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Using Common Vector Approach to Recognize Isolated Mandarin Word Based on Vowel and Consonant
|關鍵字:||梅爾頻率倒頻譜係數;Mel-frequency cepstrum coefficient;特徵擷取;共同向量法;Feature Extraction;Common vector||出版社:||統計學研究所||引用:||1 Bayrakceken M.K., Cay M.A. and Barkana A., (2007),"Word Spotting Using Common Vector Approach", Signal Processing and Communications Applications, IEEE 15th , pp. 1 - 4. 2 Cevikalp H., Neamtu M., Barkana A., (2007),"The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition", Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Trans. on Speech and Audio Processing, Vol. 37, Issue: 4 , pp. 937 - 951 3 Edizkan R., Gu ̈lmezog ̌lu M. B., Ergin S. and Barkana A., (2005),"Improvements on common vector approach for multi class problems", In 13th European Signal Processing Conference, Antalya, Turkiye. 4 Ergin S. and Gu ̈lmezog ̌lu M.B., (2007),"Face recognition based on face partitions using Common Vector Approach", Communications, Control and Signal Processing, ISCCSP 2008. 3rd International Symposium on, pp. 624-628. 5 Gu ̈lmezog ̌lu M. B., Dzhafarov V., Keskin M., and Barkana A., (1999),"A Novel Approach to Isolated Word Recognition", IEEE Trans. On Speech and Audio Processing, Vol. 7, no. 6, pp. 620-628. 6 Gu ̈lmezog ̌lu M. B., Dzhafarov V. and Barkana A., (2001),"The Common Vector Approach and Its Relation to Principal Component Analysis", IEEE Trans. On Speech and Audio Processing, Vol. 9, no. 6, pp. 655-662. 7 Fukunaga K. and Koonz W. L., (1970),"Application of the Karhuenn-Loeve expansion to feature selection and ordering", IEEE Trans. Comput., Vol. C-19, no. 4,pp. 311-318. 8 Iijima T., Genchi H., Mori K., (1973),"A theory of character recognition by pattern matching method", The Proc. of the 1st International Joint Conference on Pattern Recognition, Washington, DC, pp. 50–56. 9 Keser S. and Edizkan R., (2009)," Phonem-based isolated Turkish word recognition with subspace classifier", Signal Processing and Communications Applications Conference, IEEE 17th, pp. 93 - 96 10 Lakshmi C., Ponnavaikko M., Sundararajan M., (2009),"Improved Kernel Common Vector Method for Face Recognition", Machine Vision, ICMV ''09. Second International Conference on, pp. 13 - 17 11 Serkan G., Edizkan R., (2007),"Use of Novel Feature Extraction Technique with Subspace Classifiers for Speech Recognition", Pervasive Services, IEEE International Conference on, pp. 80 - 83 12 Watanabe S., Lambert P.F., Kulikowski C.A., Buxton J.L. and Walker R., (1967), "Evaluation and selection of variables in pattern recognition", in Compter and Information Sciences ii. New York: Academic, pp. 91. 13 Watanabe S. and pakvasa N., (1973),"Subspace method in pattern recognition", in Proc. 1st Int. Conf. Pattern Recog., Washington, DC, pp. 25-32. 14 陳漢良、李宗寶， (2012)。「利用共同向量法辨識中文單音及爆破氣音的探討」。碩 士論文，國立中興大學統計學研究所，台中。||摘要:||
This paper is to investigate the 1391 monosyllable in speaker-dependent system. The method of common vector is used for the speech recognition. The method is simple and easy in application not only for speech recognition but also for face pattern recognition. The common vector approach is a linear subspace classifier. In each class, it projects all training features into a unique common feature as the model for the corresponding class. In this paper, We use the Mel-frequency cepstrum coefficient (Mfcc) as the feature in the recognition. The mandarin monosyllable divided into consonants and vowels two part, in which the common vector approach is contructed as model, respectively. The different weights are then given for each consonant and vowel parts as the parameters in speech recognition. In the work, the set of training samples will also be considered as the parameter in recognition. From the exprimental results, we find that when the weight equals to (0.5,0.5) for consonant and vowel parts, and the training samples is 4, the best speech recognition will be up to 82%.
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