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A Phoneme-Based Approach for Eliminating Out-of-vocabulary Problem Turkish Speech Recognition Using Hidden Markov Model

Erdem Yavuz1,∗, Vedat Topuz2

1 Istanbul Commerce University, Department of Computer Engineering, 34840, Istanbul
2 Marmara University, Vocational School of Technical Sciences, 34722, Istanbul. E-mail:

* Corresponding Author: E-mail:

Computer Systems Science and Engineering 2018, 33(6), 429-445.


Since Turkish is a morphologically productive language, it is almost impossible for a word-based recognition system to be realized to completely model Turkish language. Due to the fact that it is difficult for the system to recognize words not introduced to it in a word-based recognition system, recognition success rate drops considerably caused by out-of-vocabulary words. In this study, a speaker-dependent, phoneme-based word recognition system has been designed and implemented for Turkish Language to overcome the problem. An algorithm for finding phoneme-boundaries has been devised in order to segment the word into its phonemes. After the segmentation of words into phonemes, each phoneme is separated into different sub-groups according to its position and neighboring phonemes in that word. Generated sub-groups are represented by Hidden Markov Model, which is a statistical technique, using Mel-frequency cepstral coefficients as feature vector. Since phoneme-based approach is adopted in this study, it has been successfully achieved that many out of vocabulary words could be recognized.


Cite This Article

E. Yavuz and V. Topuz, "A phoneme-based approach for eliminating out-of-vocabulary problem turkish speech recognition using hidden markov model," Computer Systems Science and Engineering, vol. 33, no.6, pp. 429–445, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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