Abstract: The existing lattice generation algorithms have no exact word end time because the Weighted Finite State Transducer (WFST) decoding networks have no word end node. An algorithm is proposed to generate the standard speech recognition lattice within the WFST decoding framework. The lattices which have no exact word end time can not be used in the keyword spotting system. In this paper, the transformation relationship between WFST phone lattices and standard word lattice is firstly studied. Afterward, a dynamic lexicon matching method is proposed to get back the word end time. Finally, a token passing method is proposed to transform the phone lattices into standard word lattices. A prune strategy is also proposed to accelerate the token passing process, which decreases the transforming time to less than 3% additional computation time above one-pass decoding. The lattices generated by the proposed algorithm can be used in not only the language model rescoring but also the keyword spotting systems. The experimental results show that the proposed algorithm is efficient for practical application and the lattices generated by the proposed algorithm have more information than the lattices generated by the comparative dynamic decoder. This algorithm has a good performance in language model rescoring and keyword spotting.