A Generalized Dynamic Composition Algorithm of Weighted Finite State Transducers for Large Vocabulary Speech Recognition

We propose a generalized dynamic composition algorithm of weighted finite state transducers (WFST), which avoids the creation of noncoaccessible paths, performs weight look-ahead and does not impose any constraints to the topology of the WFSTs. Experimental results on Wall Street Journal (WSJ1) 20k-word trigram task show that at 17% WER (moderately-wide beam width), the decoding time of the proposed approach is about 48% and 65% of the other two dynamic composition approaches. In comparison with static composition, at the same level of 17% WER, we observe a reduction of about 60% in memory requirement, with an increase of about 60% in decoding time due to extra overheads for dynamic composition.