模糊语言模型在唇读系统中的应用

论文针对传统的统计语言模型所面临的数据稀疏和估计严苛性问题，提出基于模糊表示的n-元语法模型，并将其应用于唇语识别系统中，结合隐马尔科夫模型(Hidden Markov Model)，建立了新的唇动识别模型—HFM(HMM and Fuzzy Language Model)。利用教育部语言文字应用研究所计算语言学研究室研制的语料库在线系统，制作了一个小型语料库，进行了句子识别实验。实验结果表明，HFM可使单音识别率最高提高6.5%，句子识别率最高提高22.7%，另外，采用语言模型对文字流进行解析，而不再是盲目文字匹配，单一视觉流的解析精确度达68.7%。 

The Application of Fuzzy Language Model in Lip-reading

In this paper, we present a n-gram model based on fuzzy representation, in allusion to the problem of data sparsity and sharply of maximum likelihood estimation that the traditional statistical language model confront. We apply it to the lip reading system, combine with Hidden Markov Model (HMM), establish a novel lip movement recognition model HFM(HMM and Fuzzy Language Model). A small vocabulary corpus was built by using the corpus online system developed by the Ministry of Education Institute of Applied Linguistics Computational Linguistics Research Laboratory for carrying out sentence recognition experiments. The experimental results demonstrate that HFM(did not need smoothing) can improve syllable recognition rate by up to 6.5%, and sentence recognition rate by up to 22.7%. In addition, using language model for text stream analysis, instead of blindly text matching, analytical accuracy of single visual flow can be up to 68.7%.