Generalization of specialized on-the-fly composition

In the Weighted Finite State Transducer (WFST) framework for speech recognition, we can reduce memory usage and increase flexibility by using on-the-fly composition which generates the search network dynamically during decoding. Methods have also been proposed for optimizing WFSTs in on-the-fly composition, however, these operations place restrictions on the structure of the component WFSTs. We propose extended on-the-fly optimization operations which can operate on WFSTs of arbitrary structure by utilizing a filter composition. The evaluations illustrate the proposed method is able to generate more efficient WFSTs.