TY - JFULL AU - Nasibeh Nasiri and Dawood Talebi Khanmiri PY - 2012/3/ TI - Comparison among Various Question Generations for Decision Tree Based State Tying in Persian Language T2 - International Journal of Computer and Information Engineering SP - 196 EP - 200 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/11978 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 62, 2012 N2 - Performance of any continuous speech recognition system is highly dependent on performance of the acoustic models. Generally, development of the robust spoken language technology relies on the availability of large amounts of data. Common way to cope with little data for training each state of Markov models is treebased state tying. This tying method applies contextual questions to tie states. Manual procedure for question generation suffers from human errors and is time consuming. Various automatically generated questions are used to construct decision tree. There are three approaches to generate questions to construct HMMs based on decision tree. One approach is based on misrecognized phonemes, another approach basically uses feature table and the other is based on state distributions corresponding to context-independent subword units. In this paper, all these methods of automatic question generation are applied to the decision tree on FARSDAT corpus in Persian language and their results are compared with those of manually generated questions. The results show that automatically generated questions yield much better results and can replace manually generated questions in Persian language. ER -