Computer multimedia assisted language and literature teaching using Heuristic hidden Markov model and statistical language model

Abstract

Computer technology has been used for decades in secondary education and foreign language preparation. Still, attempts to incorporate technology have presented educators with various challenges due to rapid progress in technology and occasional changes in language education methods. One way of improving student engagement is to provide connectivity throughout the teaching and learning process in a classroom, allowing students to improve their English language skills. The multimedia classroom allows students to interact with various texts, giving them a solid background intasks and material of mainstream college classes. This paper introduces a method to recognise large unconstrained handwritten text vocabulary using Heuristic Hidden Markov Model and Statistical Language model (HHMM-SLM).This provides statistical language models to be applied to enhance our system's performance. Numerous experiments with single and multiple writer data have been conducted. Language models have been used to improve system accuracy. The variable size Lexica is used (between 10,000 and 50,000 words). Further, a lexicon for English with a very precise width has been developed, which makes its contribution. Our approach is comprehensive and compared to other literature approaches to deal with the same issue.