计算机应用研究2011，Vol.28Issue(8)：2931-2934,4.DOI:10.3969/j.issn.1001-3695.2011.08.036
低空间复杂度的加权有限状态转换器合成算法
Low space-complexity composition algorithm for weighted finite-state transducers
摘要
Abstract
The WFST-related composition algorithm could be used to integrate recognition models together to facilitate the utilization of knowledge during speech recognition and to improve the recognition system's performance. The general composition algorithm stores lots of useless states and transitions during it runs. It needs 1 GB or more memory to save the useless info when compose dictionary and language models, which impact the algorithm' s space complexity. To solve this problem, this paper developed a SPCA. It improved the general composition method. With the new method, the composition and removing useless info were done simultaneously. Experiments shows that the improved method achieves 14.99% and 25.72% in average and maximum memory reduction compared with the general method, and effectively reduces the composition' s space complexity.关键词
加权有限状态转换器/合成/有向图/空间复杂度/语音识别Key words
WFST(weighted finite-state transducer)/ composition/ digraph/ space-complexity/ speech recognition分类
计算机与自动化引用本文复制引用
李伟,吴及,吕萍..低空间复杂度的加权有限状态转换器合成算法[J].计算机应用研究,2011,28(8):2931-2934,4.
