Interspeech'2005 - Eurospeech
In this paper, we present an improved semi-dynamic network decoding strategy by incorporating weighted finite-state transducer (WFST)-based search network. In our approach, a static search network is first optimized by applying WFST algorithms (determinization and minimization) to the composition of a lexicon and a language model. Then the WFST is partitioned into a set of subnetworks according to language model (LM) histories, and transformed into a subnetwork-based search network with exploiting structural differences where a WFST is a Mealy machine and our representation is essentially a Moore machine. This new strategy, which is opposite to our previous approach where each subnetwork depending on a LM history is first constructed and aggregates into a complete network, can let any static network compatible to WFST enjoy the run-time efficiency from the subnetwork-caching operation as well as the static efficiency from the WFST algorithms. The experimental results using Korean read speech dictation task are presented to show its efficiency.
Bibliographic reference. Ahn, Dong-Hoon / Oh, Su-Byeong / Chung, Minhwa (2005): "Improved semi-dynamic network decoding using WFSTs", In INTERSPEECH-2005, 577-580.