Children Specifically Language Impairment Severity Level Prediction using Improved Conditional Random Fields and Comparison with Traditional Models

Abstract

Disorders in children are naturally higher in terms of lacking food habitats, birth state itself shows the impairment like visual blindness, speech loss, hearing loss etc. Research in the medical field in the United States focuses on speech incompetency, hearing deficiency, and memory loss. Natural Language processing has many involvements to recognize the impairment stages and predictions at its earlier stages. The objective is to predict the affected Children Specifically Language Impairment (CSLI) one of the disability using the new proposed approach highlighted as Improved Conditional Random Fields (ICRF). Using the sound signal from open databases, features are converted to a corpus which is generated from collective locations. In the existing system, the feature extraction in child impairment was challenging which failed in text summarization with mathematical modelling. Automatic extraction of speech disability from speech id, audio signal conversion and complete matching of possible corrected factorization is lacking. These work with training positive feature correction by recognizing the features from supervised learning. Accurate features are evolved by applying ICRF segments that can be segregated into partitions which remove negative factorized and its matrix are identified. Results are with 89.14% accuracy to show the positive matrix combination that can calculate the speech signals in a binary formation.