Research of emotions and topic-related mixed language model about lip-reading recognition

Abstract

To improve the accuracy of lip-reading recognition, an emotions and topic-related mixed language model has been researched. On the basis of the key words, the topic is divided by subject words, improved scene training corpus design and parameter estimation methods are used, the scene training corpus of different topics is expressed as the fuzzy subset of the whole scene training corpus, parameter estimated which can be got is also based on the fuzzy training set of different topics. The problem of sparse data which is introduced by less of training corpus in traditional language model has been eased by improved methods, quantitative description about the relationship of scene training corpus and topics has been presented, and full use of the image identification techniques for expression recognition in lipreading recognition area, auxiliary emotional factors language model to carry out lip-reading recognition.