Sub-lexical modelling using a finite state transducer framework

The finite state transducer (FST) approach [1] has been widely used recently as an effective and flexible framework for speech systems. In this framework, a speech recognizer is represented as the composition of a series of FSTs combining various knowledge sources across sub-lexical and high-level linguistic layers. In this paper, we use this FST framework to explore some sublexical modelling approaches, and propose a hybrid model that combines an ANGIE [2] morpho-phonemic model with a lexiconbased phoneme network model. These sub-lexical models are converted to FST representations and can be conveniently composed to build the recognizer. Our preliminary perplexity experiments show that the proposed hybrid model has the advantage of imposing strong constraints to the in-vocabulary words as well as providing detailed sub-lexical syllabification and morphology analysis of the out-of-vocabulary (OOV) words. Thus it has the potential of offering good performance and can better handle the OOV problem in speech recognition.