TY  - JOUR
T1  - Large Vocabulary Arabic Continuous Speech Recognition using Tied States
Acoustic Models
AU - Azim, Mona A. AU - Badr, Nagwa L. AU - A. Hamid, A. Aziz AU - Tolba, M.F. 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 2
SP  - 49
EP  - 56
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.49.56
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.49.56
KW  - speech signals
KW  -tri-phone
KW  -speech recognition
KW  -Straight forward
KW  -benchmark
KW  -Arabic phonetic
AB  - The Hidden Markov Model (HMM) lies at the heart of the modern speech recognition systems as
it provides a simple, effective and straight forward frame work to model the time varying acoustic features of
the speech signals. The basic process of building HMM based speech recognition systems is a straight forward
process. Nevertheless, the proper parameter estimation of such models requires large training data. Therefore,
parameter tying techniques were developed to reduce the parameters of HMMs without affecting the overall
system performance. This study proposes an Arabic phonetic decision tree necessary to build Tied State
tri-phone HMMs. Experimental results show promising word correctness when compared with both data driven
tri-phone models and phoneme based models. The maximum word correctness achieved by the proposed
approach was 95.13%. Whereas it reached 78.03 and 58.45% using data driven tri-phones and phoneme based
HMMs, respectively, when tested on the same benchmark database.
ER  -