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http://dx.doi.org/10.6109/jkiice.2013.17.4.775

Speaker-dependent Speech Recognition Algorithm for Male and Female Classification  

Choi, Jae-Seung (신라대학교 전자공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.4, 2013 , pp. 775-780 More about this Journal
Abstract
This paper proposes a speaker-dependent speech recognition algorithm which can classify the gender for male and female speakers in white noise and car noise, using a neural network. The proposed speech recognition algorithm is trained by the neural network to recognize the gender for male and female speakers, using LPC (Linear Predictive Coding) cepstrum coefficients. In the experiment results, the maximal improvement of total speech recognition rate is 96% for white noise and 88% for car noise, respectively, after trained a total of six neural networks. Finally, the proposed speech recognition algorithm is compared with the results of a conventional speech recognition algorithm in the background noisy environment.
Keywords
Speaker-dependent speech recognition algorithm; neural network; LPC cepstrum coefficients; white and car noise;
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1 A. A. M. Abushariah, T. S. Gunawan, O. O. Khalifa and M. A. M. Abushariah, "English digits speech recognition system based on Hidden Markov Models", 2010 International Conference on Computer and Communication Engineering, pp. 1-5, May 2010.
2 D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagation errors", Nature, Vol. 323, pp. 533-536, 1986.   DOI   ScienceOn
3 T. T. Le, J. S. Mason and T. Kitamura, "Characteristics of multi-layer perceptron models in enhancing degraded speech", Proc. ICSLP-94, pp. 1611-1614, 1994.
4 L. Yang, L. Jing, Y. Yuxiang and W. Jian, "Improvement algorithm of DTW on isolated-word recognition", 2011 IEEE International Conference on Computer Science and Automation Engineering, Vol. 3, pp. 319-322, 2011.
5 Y. M. Zeng, Z. Y. Wu, T. Falk and W. Y. Chan, "Robust GMM based gender classification using pitch and RASTA-PLP parameters of speech", 2006 International Conference on Machine Learning and Cybernetics, pp. 3376-3379, August 2006.
6 C. C. Chen, P. T. Lu, M. L. Hsia, J. Y. Ke and O.T.-C. Chen, "Gender-to-Age hierarchical recognition for speech", 2011 IEEE 54th International Midwest Symposium on Circuits and Systems, pp. 1-4, 2011.
7 M. Kos, M.; D. Vlaj and Z. Kacic, "Speaker's gender classification and segmentation using spectral and cepstral feature averaging", 2011 18th International Conference on Systems, Signals and Image Processing, pp. 1-4, 2011.
8 H. Xu, X. Zhang and L. Jia, "The extraction and simulation of Mel frequency cepstrum speech parameters", 2012 International Conference on Systems and Informatics, pp. 1765-1768, 2012.
9 P. B. Patil, "Multilayered network for LPC based speech recognition", IEEE Transactions on Consumer Electronics, Vol. 44, No. 2, pp. 435-438, 1998.   DOI   ScienceOn
10 H. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy conditions", in Proc. ISCA ITRW ASR2000 on Automatic Speech Recognition: Challenges for the Next Millennium, Paris, France, 2000.