single-jc.php

JACIII Vol.16 No.3 pp. 381-387
doi: 10.20965/jaciii.2012.p0381
(2012)

Paper:

Fuzzy Hidden Markov Models for Indonesian Speech Classification

Intan Nurma Yulita*,** Houw Liong The**, and Adiwijaya**

*Faculty of Informatics, Telkom Institute of Technology

**Graduate Faculty, Telkom Institute of Technology, Jalan Telekomunikasi No.1, DayeuhKolot, Jawa Barat 40257, Indonesia

Received:
September 15, 2011
Accepted:
November 15, 2011
Published:
May 20, 2012
Keywords:
fuzzy logic, hidden Markov models, speech, classification, clustering
Abstract

Indonesia has many tribes, so that there are many dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. The solution is a new combination of fuzzy on hidden Markov models. The result shows a new version of fuzzy hiddenMarkovmodels is better than hidden Markov model.

References
  1. [1] S. D. Shenouda, F. W. Zaki, and A. Goneid, “Hybrid Fuzzy HMm System for Arabic Connectionist Speech Recognition,” Proc. of the 5th WSEAS Int. Conf. on Signal Processing, robotics and Automation, pp. 64-69, 2006.
  2. [2] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. of the IEEE, Vol.77, No.2, 1989.
  3. [3] P.Melin, J. Urias, D. Solano, et al., “Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms,” Engineering Letters, Vol.13, No.2, 2006.
  4. [4] L. Chen, S. Gunduz, and M. T. Ozsu, “Mixed Type Audio Classification with Support Vector Machine,” Proc. of the IEEE Int. Conf. on Multimedia and Expo, 2006.
  5. [5] R. Halavati, S. B. Shouraki, M. Eshraghi, and M. Alemzadeh, “A Novel Fuzzy Approach to Speech Processing,” 5th Hybrid Intelligent Systems Conf., 2004.
  6. [6] S. E. Levinson, L. R. Rabiner, A. E. Rosenberg, and J. G. Wilpon, “Interactive Clustering Techniques for Selecting Speaker-Independent Reference Templates For Isolated Word Recognition,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.Assp-27, 1979.
  7. [7] B. H. Juang and L. R. Rabiner, “Fundamentals of Speech Recognition,” Prentice-Hall, 1993.
  8. [8] B. H. Juang and L. R. Rabiner, “Hidden Markov Models for Speech Recognition,” Technometrics, Vol.33, No.3, pp. 251-272, 1991.
  9. [9] J. Zeng and Z.-Q. Liu, “Interval Type-2 Fuzzy Hidden Markov Models,” Proc. of Int. Conf. on Fuzzy Systems, Vol.2, pp. 1123-1128, 2004.
  10. [10] J. Zeng and Z.-Q. Liu, “Type-2 Fuzzy Hidden Markov Models to Phoneme Recognition,” Proc. of the 17th Int. Conf. on Pattern Recognition, 2004.
  11. [11] H. Riza and O. Riandi, “Toward Asian Speech Translation System: Developing Speech Recognition and Machine Translation for Indonesian Language,” Int. Joint Conf. on Natural Language Processing, 2008.
  12. [12] D. P. Lestari, K. Iwano, and S. Furui, “A Larger Vocabulary Continuous Speech Recognition System for Indonesian Language,” 15th Indonesian Scientific Conf. in Japan Proceedings, 2006.
  13. [13] H. Uguz, A. Ozturk, R. Saracoglu, and A. Arslan, “A Biomedical System Based on Fuzzy Discrete HiddenMarkov Model for The Diagnosis of The Brain Diseases,” Expert SystemsWith Applications, Vol.35, pp. 1104-1114, 2008.
  14. [14] S. Kusumadewi, H. Purnomo, and A. Logika, “Fuzzy untuk Pendukung Keputusan,” Penerbit Graha Ilmu, pp. 84-85, 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Sep. 21, 2017