A phoneme-based approach for eliminating out-of-vocabulary problem of Turkish speech recognition using Hidden Markov Model
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
C R L Publishing LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Speech recognition, Hidden Markov model, cepstral analysis, phoneme boundary
Kaynak
Computer Systems Science and Engineering
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
33
Sayı
6
