In this study, we will interest in phonemes classification of Timit database using Fuzzy Logic. The fuzzy method consists in the extraction of a three fuzzy-reference vectors: maximal, mean and minimal. To classify a phoneme request, we calculate its degree of membership to all defined classes. The class of a phoneme request is, then, the one which maximizes one degree of membership calculated according to reference vectors. Different techniques of speech analysis are used for evaluation. Results show that fuzzy logic can provide a significant issue when mathematical rigor is not present as to the signal processing since the retained recognition rates was 90,85%, 22,96%, 98,57% and 91,73% for respectively MFCC, LPC, PLP and RASTA PLP.
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 2, Issue 1) |
| DOI | 10.11648/j.cssp.20130201.11 |
| Page(s) | 1-5 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Fuzzy Logic; MFCC; LPC; PLP; RASTA-PLP; Speech; Timit
| [1] | Anusuya, M.A., and Katti, S.K. "Front end analysis of speech recognition: a review", International Journal of Speech Technology., 2011, pp. 99–145. |
| [2] | Juang, B. H., and Rabiner, L.R.: "Automatic speech recognition - A brief history of the technology development" (Elsevier Encyclopedia of Language and Linguistics, 2005). |
| [3] | Gajsek, R., and Mihelič, F.: "Comparison of speech parameterization techniques for Slovenian language". Proc. 9th International PhD Workshop on Systems and Control: Young Generation Viewpoint, Slovenia, 2008. |
| [4] | Veeravalli, A.G., Pan, W.D., Adhami, R., and Cox, P.G.: "A tutoriel on using hidden markov models for phoneme recognition". Proc. Thirty-Seventh Southeastern Symposium on System Theory (SSST05), 2005, pp. 154 – 157. |
| [5] | Meyer, B.T., and Kollmeier, B.: "Complementarity of MFCC, PLP and Gabor features in the presence of speech-intrinsic variabilities". Proc. Interspeech, Brighton, 2009. |
| [6] | Hachkar, Z., Mounir, B., Farchi, A., and El Abbadi, J.: "Comparison of MFCC and PLP Parameterization in pattern recognition of Arabic Alphabet Speech", Canadian Journal on Artificial Intelligence, Machine Learning & Pattern Recognition., 2011. |
| [7] | Thiang., and Wijoyo, S.:»Speech Recognition Using Linear Predictive Coding and Artificial Neural Network for Controlling Movement of Mobile Robot". Proc. International Conference on Information and Electronics Engineering, Singapore, 2011, pp. 179-183. |
| [8] | H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech," J. Acoust. Soc. Am, pp 1738-1752, 1990. |
| [9] | H. Hermansky, N. Morgan, A. Bayya and P. Kohn, "Rasta PLP Speech Analysis," TR-91-069, Dec 1991. |
| [10] | L.A. Zadeh, "Fuzzy logic, neural networks, and soft computing," ACM’94, March 1994, vol.37, no 3, pp.77-84. |
| [11] | L.A. Zadeh, "Fuzzy sets," Information and control, vol. 8, no.3, pp. 338-353, June 1965. |
| [12] | L.A. Zadeh, "Making computers think like people," IEEE. Spectrum, 8/1984, pp. 26-32. |
| [13] | http://www.ferdinandpiette.com/blog/2011/08/la-logique-floue-interets-et-limites/, accessed: November 2012. |
| [14] | B. Paris, J. Eynard, G. François, T. Talbert, A. Traore and F. Thiery, "Gestion des ressources énergétiques d’un bâtiment : contrôle flou," IBPSA, France, 2008. |
| [15] | http://www.ldc.upenn.edu/Catalog/readme_files/timit.readme.html, accessed: November 2012. |
| [16] | A. Sadiq, R.O.H. Thami, M. Daoudi, J.P. Vandeborre, "Classification des Objets 3D Basée sur la Logique Floue," Compression et Représentation des Signaux Audiovisuels (CORESA'2004), Lille, France, 25-26 mai 2004. |
| [17] | D.P.W.Ellis. PLP and RASTA (and MFCC, and inversion) in Matlab using melfcc.m and invmelfcc.m. 143 http://www.ee.columbia.edu/ ~dpwe/ resources/ matlab/ rastamat/ accessed: November 2012. |
| [18] | A. Strassberg, M. Molochnikov and R. Steinberg, "Recognition of plosives using Fuzzy-Logic," Signal and Image processing Lab SIPL, 2009. |
| [19] | H.K. Tripathy, B.K. Tripathy and P.K. Das, "A Knowledge based Approach Using Fuzzy Inference Rules for Vowel Recognition," Journal of Convergence Information Technology, vol. 3, no. 1, Mar. 2008. |
| [20] | M. Chetouani, B. Gas, J.L.Zarader and C.Chavy, "Neural Predictive Coding for Speech Discriminant Feature Extraction: The DFE-NPC," European Symposium on Artificial Neural Networks ESANN, pp. 275-280, Belgium, 24-26 April 2002. |
APA Style
Ines Ben Fredj, Kaïs Ouni. (2013). A Novel Phonemes Classification Method Using Fuzzy Logic. Science Journal of Circuits, Systems and Signal Processing, 2(1), 1-5. https://doi.org/10.11648/j.cssp.20130201.11
ACS Style
Ines Ben Fredj; Kaïs Ouni. A Novel Phonemes Classification Method Using Fuzzy Logic. Sci. J. Circuits Syst. Signal Process. 2013, 2(1), 1-5. doi: 10.11648/j.cssp.20130201.11
AMA Style
Ines Ben Fredj, Kaïs Ouni. A Novel Phonemes Classification Method Using Fuzzy Logic. Sci J Circuits Syst Signal Process. 2013;2(1):1-5. doi: 10.11648/j.cssp.20130201.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.cssp.20130201.11,
author = {Ines Ben Fredj and Kaïs Ouni},
title = {A Novel Phonemes Classification Method Using Fuzzy Logic},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {2},
number = {1},
pages = {1-5},
doi = {10.11648/j.cssp.20130201.11},
url = {https://doi.org/10.11648/j.cssp.20130201.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20130201.11},
abstract = {In this study, we will interest in phonemes classification of Timit database using Fuzzy Logic. The fuzzy method consists in the extraction of a three fuzzy-reference vectors: maximal, mean and minimal. To classify a phoneme request, we calculate its degree of membership to all defined classes. The class of a phoneme request is, then, the one which maximizes one degree of membership calculated according to reference vectors. Different techniques of speech analysis are used for evaluation. Results show that fuzzy logic can provide a significant issue when mathematical rigor is not present as to the signal processing since the retained recognition rates was 90,85%, 22,96%, 98,57% and 91,73% for respectively MFCC, LPC, PLP and RASTA PLP.},
year = {2013}
}
TY - JOUR T1 - A Novel Phonemes Classification Method Using Fuzzy Logic AU - Ines Ben Fredj AU - Kaïs Ouni Y1 - 2013/02/20 PY - 2013 N1 - https://doi.org/10.11648/j.cssp.20130201.11 DO - 10.11648/j.cssp.20130201.11 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 1 EP - 5 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20130201.11 AB - In this study, we will interest in phonemes classification of Timit database using Fuzzy Logic. The fuzzy method consists in the extraction of a three fuzzy-reference vectors: maximal, mean and minimal. To classify a phoneme request, we calculate its degree of membership to all defined classes. The class of a phoneme request is, then, the one which maximizes one degree of membership calculated according to reference vectors. Different techniques of speech analysis are used for evaluation. Results show that fuzzy logic can provide a significant issue when mathematical rigor is not present as to the signal processing since the retained recognition rates was 90,85%, 22,96%, 98,57% and 91,73% for respectively MFCC, LPC, PLP and RASTA PLP. VL - 2 IS - 1 ER -
Information
Email: