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.
Cite as: Ahn, D.-H., Oh, S.-B., Chung, M. (2005) Improved semi-dynamic network decoding using WFSTs. Proc. Interspeech 2005, 577-580, doi: 10.21437/Interspeech.2005-345
@inproceedings{ahn05_interspeech,
author={Dong-Hoon Ahn and Su-Byeong Oh and Minhwa Chung},
title={{Improved semi-dynamic network decoding using WFSTs}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={577--580},
doi={10.21437/Interspeech.2005-345}
}