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Voice Activated E-Learning System for the Visually Impaired

by S.Asha, C.Chellappan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 7
Year of Publication: 2011
Authors: S.Asha, C.Chellappan
10.5120/1892-2514

S.Asha, C.Chellappan . Voice Activated E-Learning System for the Visually Impaired. International Journal of Computer Applications. 14, 7 ( February 2011), 42-51. DOI=10.5120/1892-2514

@article{ 10.5120/1892-2514,
author = { S.Asha, C.Chellappan },
title = { Voice Activated E-Learning System for the Visually Impaired },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 14 },
number = { 7 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 42-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number7/1892-2514/ },
doi = { 10.5120/1892-2514 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:48.478885+05:30
%A S.Asha
%A C.Chellappan
%T Voice Activated E-Learning System for the Visually Impaired
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 7
%P 42-51
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

E-learning has become an important tool for learners to acquire information and knowledge. However visually impaired people have no or very little access to this tool, since interface suitable to them are unavailable. The Voice Activated E learning System can provide a solution to this problem. Developing this system is meant to assist visually impaired students in learning, a desired subject, from the system, in a convenient way using their voice commands. This system consists of two major subsystems; namely Speaker Verification and Speech Recognition subsystem. In the speaker verification subsystem, Mel-Frequency Cepstral Coefficients (MFCC) is used for Feature extraction and Vector Quantization (VQ) algorithm is used for codebook generation. In the speech recognition subsystem, MFCC and dynamic programming (DP) are used. Experimental results show an accuracy of 96% in speaker verification subsystem and 89% in speech recognition subsystem.

References
  1. Atal .B. S (1972), Automatic Speaker Recognition Based on Pitch Contours, International Acoustics Society, America, vol. 52, pp. 1-687.
  2. Athanasios Drigas, Leyteris Koukianakis, Yannis Papagerasimou, “An E-Learning Environment for Nontraditional Students with Sight Disabilities”, 36th ASEE/IEEE Frontiers in Education Conference, IEEE 2006.
  3. Belfield .W. R. and Mikkilineni .R. P. (1997), Speaker Verification Based On A Vector Quantization Approach That Incorporates Speaker Cohort Models And A Linear Discriminator, IEEE, Vol.1, 4525-4529.
  4. Bing Xiang, Toby Berger (2003), Efficient Text-Independent Speaker Verification with Structural Gaussian Mixture Models and Neural Network, IEEE Transactions on Speech and Audio Processing, Vol. 11, No. 5, IEEE.
  5. Charlet.D, Jouvet.D, Collin.O (2000), An Alternative Normalization Scheme in HMM-Based Text-Dependent Speaker Verification, Speech Communication, Elsevier publications, Vol. 31, pp. 113-120.
  6. Clarence Goh Kok Leon, Politeknik Seberang Perai, Jalan Permatang Pauh, “Robust Computer Voice Recognition Using Improved MFCC Algorithm”, International Conference on New Trends in Information and Service Science, IEEE 2009.
  7. Enric Monte-Moreno, Mohamed Chetouani, Marcos Faundez-Zanuy, Jordi Sole-Casals (2009), Maximum Likelihood Linear Programming Data Fusion for Speaker Recognition, Speech Communication, Vol. 51, pp. 820-830.
  8. France and Janez, (2008), A textbook on Speech Recognition Technologies and Applications.
  9. George R. Doddington, Mark A. Przybocki, Alvin F. Martin, Douglas A. Reynolds (2000), The NIST Speaker Recognition Evaluation-Overview, Methodology, Systems, Results, Perspective, Speech Communication, Vol. 31, pp. 225-254.
  10. Gurmeet Singh, Ashish Panda, Sauruv Bhattuchulyya, Thambipillui Stikunthun (2003), Vector Quantization Techniques for GMM Based Speaker Verification, ICASSP 2003, IEEE 2003, pp. 65 - 68.
  11. He, J., Liu, L., Palm, G. (1999), A Discriminative Training Algorithm for VQ-Based Speaker Identification, IEEE Transactioons, Speech Audio Process, 7 (3), 353-356.
  12. Hemant Misra, Shajith Ikbal, B. Yegnanarayana (2003), Speaker-Specific Mapping for Text-Independent Speaker Recognition, Speech Communication, Vol.39, pp 301-310.
  13. Hou Fenglei, Wang Bingxi (2002), Text Independent Speaker Verification Using Speaker Clustering and Support Vector Machines, the Proceedings of ICSP’02, IEEE 2002, pp 456-459.
  14. Jon Barker a, Ning Maa, Andre´ Coy a, Martin Cooke (2010), Speech Fragment Decoding Techniques for Simultaneous Speaker Identification and Speech Recognition, Computer Speech and Language, Vol.24, pp 94-111.
  15. Leandro Rodrguez-Linaresa, Carmen Garca-Mateob, Jose Luis Alba-Castrob (2003), On Combining Classifiers for Speaker Authentication, Pattern Recognition, Vol. 36, pp 347-359.
  16. Longbiao Wang, Norihide Kitaoka, Seiichi Nakagawa (2007), Robust Distant Speaker Recognition Based on Position-Dependent CMN by Combining Speaker-Specific GMM With Speaker-Adapted HMM, Speech Communication, Vol.49, pp. 501-513.
  17. Masaki Naito, Li Deng, Yoshinori Sagisaka (2002), Speaker Clustering for Speech Recognition Using Vocal Tract Parameters, Speech Communication, Vol. 36, pp. 305-315
  18. Mohammad Nouman, Nasir Naveed, Muhammad Abdul Basit Khan,“E-Learning for Visually Impaired”, University of Pakistan, 2006.
  19. Paul Cristea and Zica Vrilsan (1999), New Cepstrum Frequency Scale For Neural Network Speaker Verification, IEEE, vol. 1, pp. 573-576.
  20. Reynolds, D., Quatieri, T., Dunn, R.(2000), Speaker Verification Using Adapted Gaussian Mixture Models. Digit. Signal Process, 10 (1), pp.19-41.
  21. Robert Batusek and Ivan Kopecek, “User Interfaces for the Visually Impaired people”, Masaryk University, 2000.
  22. Rosenberg A.E, "Automatic Speaker Verification: A Review," Proc. IEEE, vol. 64, pp. 475-487, Apr. 1976.
  23. M. Savic J. SoFensen 91992), Phoneme Based Speaker Verification, IEEE, Vol. 2 pp. 165-168.
  24. Shung-Yung Lung (2007), Efficient Text Independent Speaker Recognition with Wavelet Feature Selection Based Multilayered Neural Network Using Supervised Learning Algorithm, Pattern Recognition, Vol. 40 pp. 3616-3620.
  25. Soong, F.K., Rosenberg, A.E., Juang, B.H., Rabiner, L.R. (1987), A Vector Quantization Approach to Speaker Recognition. AT&T Tech. J. 66, pp.14-26.
  26. Tuan Pham, Michael Wagnel (2000), Information Based Speaker Verification, IEEE, pp. 278-281.
  27. Wang Yutai, Li Bo, Jiang Xiaoqing, Liu Feng, Wang Lihao, “Speaker Recognition Based on Dynamic MFCC Parameters”, IEEE 2009.
  28. Yi-Hsiang Chao, Wei-HoTsaic, Hsin-MinWang, Ruei-ChuanChang (2009), Improving the Characterization of the Alternative Hypothesis Via Minimum Verification Error Training with Applications to Speaker Verification, Pattern Recognition, Vol. 42, pp. 1351 - 1360.
Index Terms

Computer Science
Information Sciences

Keywords

Voice E-Learning Mel-Frequency Cepstral Coefficients (MFCC) Vector Quantization (VQ)

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