Classification of Malaysian Vowels Using Formant Based Features

Authors

  • S. A. Mohd Yusof School of Mechatronics Engineering, Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
  • Paulraj M School of Mechatronics Engineering, Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia
  • S. Yaacob School of Mechatronics Engineering, Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia

DOI:

https://doi.org/10.32890/jict2008.7.2

Keywords:

Speech recognition, Vowel detection, Formant

Abstract

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software, especially using English as the language of choice. Despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. In this paper, a new feature based on formant is presented and evaluated on Malaysian spoken vowels. These features were classified and used to identify vowels recorded from 80 Malaysian speakers. A back propagation neural network (BPNN) model was developed to classify the vowels. Six formant features were evaluated, which were the first three formant frequencies and the distances between each of them. Results, showed that overall vowel classification rate of these three formant combinations are comparatively the same but differs in terms of individual vowel classification.

 

References

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Published

07-04-2008

Issue

Vol. 7 (2008): Journal of Information and Communication Technology (JICT) Vol. 7, 2008

Section

Articles

How to Cite

Mohd Yusof, S. A., M, P., & Yaacob, S. (2008). Classification of Malaysian Vowels Using Formant Based Features. Journal of Information and Communication Technology, 7, 27-40. https://doi.org/10.32890/jict2008.7.2

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2008.7.2