Query-based composition for large-scale language model in LVCSR

This paper describes a query-based composition algorithm that can integrate an ARPA format language model in the unified WFST framework, which avoids the memory and time cost of converting the language models to WFST and optimizing the WFST of language models. The proposed algorithm is applied to on-the-fly one-pass decoder and rescoring decoder. Both modified decoder require less memory during decoding on different scale of language models. What´s more, query-based on-the-fly one-pass decoder nearly has the same decoding speed as standard one and query-based rescoring decoder even use less time to rescore the lattice. Because of these advantages, large-scale language models can be applied by query-based composition algorithm to improve the performance of large vocabulary continuous speech recognition.