Abstract
This paper presents the advantages of augmenting a word-based system with sub-word units as a step towards building open vocabulary speech recognition systems. We show that a hybrid system which combines words and data-driven, variable length sub word units has a better phone accuracy than word only systems. In addition the hybrid system is better in detecting Out-Of-Vocabulary (OOV) terms and representing them phonetically. Results are presented on the RT-04 broadcast news and MIT Lecture data sets. An FSM-based approach to recover OOV words from the hybrid lattices is also presented. At an OOV rate of 2.5% on RT-04 we observed a 8% relative improvement in phone error rate (PER), 7.3% relative improvement in oracle PER and 7% relative improvement in WER after recovering the OOV terms. A significant reduction of 33% relative in PER is seen in the OOV regions.
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