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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 871324, 8 pages
Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework
Department of Electronics and Communication Technology, Gauhati University, Assam, Guwahati 781014, India
Received 18 April 2012; Accepted 1 October 2012
Academic Editor: F. Morabito
Copyright © 2012 Mousmita Sarma and Kandarpa Kumar Sarma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- K. O. E. Elenius and H. G. C. Traven, Multi-Layer Perceptrons and Probabilistic Neural Networks for Phoneme Recognition, Department of Speech Communication and Music Acoustics, Department of Numerical Analysis and Computing Science (NADA), Stockholm, Sweden, 1993.
- N. Mammone, F. La Foresta, and F. C. Morabito, “Automatic artifact rejection from multichannel scalp EEG by Wavelet ICA,” IEEE Sensors Journal, vol. 12, no. 3, pp. 533–542, 2012.
- D. Labate, F. La Foresta, G. Inuso, and F. C. Morabito, “Remarks about wavelet analysis in the EEG artifacts detection,” Frontiers in Artificial Intelligence and Applications, vol. 226, pp. 99–106, 2011.
- S. C. Ng and P. Raveendran, “Enhanced mu rhythm extraction using blind source separation and wavelet transform.,” IEEE Transactions on Bio-Medical Engineering, vol. 56, no. 8, pp. 2024–2034, 2009.
- N. P. Castellanos and V. A. Makarov, “Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis,” Journal of Neuroscience Methods, vol. 158, no. 2, pp. 300–312, 2006.
- B. Azzerboni, G. Finocchio, M. Ipsale, F. La Foresta, and F. C. Morabito, “A new approach to detection of muscle activation by Independent Component Analysis and wavelet transform,” in Neural Nets, vol. 2486 of Lecture Notes in Computer Science, pp. 109–116, 2002.
- G. C. Goswami, Structure of Assamese, Department of publication, Gauhati University, Assam, India, 1st edition, 1982.
- Courtesy: Prof. Gautam Baruah, http://tdil.mit.gov.in/assamesecodechartoct02.pdf, Dept. of CSE, IIT Guwahati, India.
- A. M. de Lima Arajo and F. Violaro, Formant Frequency Estimation Using A Mel Scale Lpc Algorithm, DECOM-FEEC-UNICAMP, São Paulo, Brazil, 2002.
- L. R. Rabiner and R. W. Schafer, Digital Processing of Speech signals, Third Impression, Pearson Education, Upper Saddle River, NJ, USA, 2009.
- M. Misiti, Y. Misiti, G. Oppenheim, and J. Poggi, Wavelet Toolbox, Users Guide, http://web.mit.edu/1.130/WebDocs/wavelet_ug.pdf, 1996.
- D. Sripathi, Chapter 2 ,The Discrete Wavelet Transform, http://etd.lib.fsu.edu/theses/available/etd-11242003-185039/unrestricted/09_ds_chapter2.pdf, 2003.
- D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, UK, 2000.
- S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, New York, NY, USA, 1999.
- T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.
- S. Haykin, Neural Network and Learning Machine, PHI Learning Private Limited, New Delhi, India, 3rd edition, 2009.
- D. F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, no. 1, pp. 109–118, 1990.
- B. T. Tang, R. Lang, H. Schroder, A. Spray, and P. Dermody, “Applying wavelet analysis to speech segmentation and classification, wavelet applications,” in Proceedings of SPIE, vol. 2242, pp. 750–761, 1994.
- R. C. Snell and F. Milinazzo, “Formant location from LPC analysis data,” IEEE Transactions on Speech and Audio Processing, vol. 1, no. 2, pp. 129–134, 1993.
- N. Ur Rehman and D. P. Mandic, “Filter bank property of multivariate empirical mode decomposition,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 2421–2426, 2011.
- M. Campolo, D. Labate, F. La Foresta, F. C. Morabito, A. Lay-Ekuakille, and P. Vergallo, “ECG-derived respiratory signal using Empirical Mode Decomposition,” in Proceedings of the IEEE International Symposium on Medical Measurements and Applications (MeMeA '11), article 5966727, 2011.
- C. D. Blakely, A Fast Empirical Mode Decomposition Technique for Nonstationary Nonlinear Time Series, Elsevier Science, New York, NY, USA, 2005.