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http://dx.doi.org/10.6109/jkiice.2012.16.7.1370

Comparison of Characteristic Vector of Speech for Gender Recognition of Male and Female  

Jeong, Byeong-Goo (목포대학교 대학원 전기공학과)
Choi, Jae-Seung (신라대학교 전자공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.16, no.7, 2012 , pp. 1370-1376 More about this Journal
Abstract
This paper proposes a gender recognition algorithm which classifies a male or female speaker. In this paper, characteristic vectors for the male and female speaker are analyzed, and recognition experiments for the proposed gender recognition by a neural network are performed using these characteristic vectors for the male and female. Input characteristic vectors of the proposed neural network are 10 LPC (Linear Predictive Coding) cepstrum coefficients, 12 LPC cepstrum coefficients, 12 FFT (Fast Fourier Transform) cepstrum coefficients and 1 RMS (Root Mean Square), and 12 LPC cepstrum coefficients and 8 FFT spectrum. The proposed neural network trained by 20-20-2 network are especially used in this experiment, using 12 LPC cepstrum coefficients and 8 FFT spectrum. From the experiment results, the average recognition rates obtained by the gender recognition algorithm is 99.8% for the male speaker and 96.5% for the female speaker.
Keywords
Gender recognition; neural network; LPC coefficients; FFT coefficients;
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  • Reference
1 J. He, L. Liu, and G. Palm, "On the use of residual cepstrum in speech recognition," IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 5-8, 1996.
2 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.
3 Q. Lin and C. Che, "Normalizing the vocal tract length for speaker independent speech recognition", IEEE Signal Processing Letters, Vol. 2, No. 11, pp. 201-203, 1995.   DOI   ScienceOn
4 J. Onshaunjit and J. Srinonchat, "LSP Trajectory Analysis for Speech Recognition", '08. Fifth International Conference on Computer Graphics, Imaging and Visualisation, pp. 276-279, 2008.
5 A. A. M. Abushariah, T. S. Gunawan, O. O. Khalifa, and A. A. M. Abushariah, "English digits speech recognition system based on Hidden Markov Models", International Conference on Computer and Communication Engineering, pp. 1-5, 2010.
6 J. G. van Velden and G. F. Smoorenburg, "Vowel recognition in noise for male, female and child voices", International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 989-992, 1991.
7 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
8 F. Hassan, M.R.A. Kotwal, and M.N. Huda, "Bangla ASR design by suppressing gender factor with gender-independent and gender-based HMM classifiers", World Congress on Information and Communication Technologies, pp. 1276-1281, 2011.
9 Y. Konig and N. Morgan, "GDNN: a genderdependent neural network for continuous speech recognition", International Joint Conference on Neural Networks, Vol. 2, pp. 332-337, 1992.
10 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