OOV Word Detection using Hybrid Models with Mixed Types of Fragments
This paper presents initial studies to improve the out-of-vocabulary (OOV) word detection performance by using mixed types of fragment units in one hybrid system. Three types of fragment units, subwords, syllables, and graphones, were combined in two different ways to build the hybrid lexicon and language model. The experimental results show that hybrid systems with mixed types of fragment units perform better than hybrid systems using only one type of fragment unit. After comparing the OOV word detection performance with the number and length of fragment units of each system, we proposed future work to better utilize mixed types of fragment units in a hybrid system.