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- Title
Feature extraction from behavioral styles of children for prediction of severity of stuttering using historical stuttering data.
- Authors
Abdul Waheed, Shaikh; Khader, P. Sheik Abdul; Khan, Abdul Azeez; Sathick, Javubar
- Abstract
Behavioral styles have a significant impact on the entire growth of human life. Literature suggests that some behavior styles affect the language development of children in the initial period of life. Therefore, the present investigations seek to analyze the behavioral styles of children to explore the impact of these styles on the severity of stuttering. Along with feature selection algorithms, the famous machine learning algorithm, namely, Ordinal Logistic Regression has been proposed for exploring the contribution of behavioral styles in the recovery and persistence of speech disorders in school-aged children. To generate valid results, the most suitable children-based publicly available dataset from Vanderbilt University was selected. The promoted dataset contains historical data records of behavioral styles of school-aged children between 3 and 7 years. The results of the present study suggest that historical data can be utilized to explore the unknown behind the onset of stuttering. Parents' education and occupation did not show any meaningful impact on the forecasting of children's stuttering severity. If children who stutter dream to recover from stuttering, they need to improve their behavioral styles like intensity, mood, persistence, distraction, surgency, effortful control, sleep problems, attention problems, and aggressive behavior.
- Subjects
VANDERBILT University; FEATURE extraction; STUTTERING; CHILDREN'S language; SPEECH disorders; SCHOOL children; FAILURE mode &; effects analysis
- Publication
International Journal of Speech Technology, 2022, Vol 25, Issue 4, p803
- ISSN
1381-2416
- Publication type
Article
- DOI
10.1007/s10772-021-09868-2