RESEARCH ARTICLE
A Novel Application System of Assessing the Pronunciation Differences Between Chinese Children and Adults
Xiaoyang Zhang1, Lei Xue1, *, Zhi Zhang1, Yiwen Zhang2
Article Information
Identifiers and Pagination:
Year: 2016Volume: 10
First Page: 91
Last Page: 100
Publisher ID: TOBEJ-10-91
DOI: 10.2174/1874120701610010091
Article History:
Received Date: 01/08/2015Revision Received Date: 25/04/2016
Acceptance Date: 01/06/2016
Electronic publication date: 04/08/2016
Collection year: 2016
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
Background:
Health problems about children have been attracting much attention of parents and even the whole society all the time, among which, child-language development is a hot research topic. The experts and scholars have studied and found that the guardians taking appropriate intervention in children at the early stage can promote children’s language and cognitive ability development effectively, and carry out analysis of quantity. The intervention of Artificial Intelligence Technology has effect on the autistic spectrum disorders of children obviously.
Objective and Methods:
This paper presents a speech signal analysis system for children, with preprocessing of the speaker speech signal, subsequent calculation of the number in the speech of guardians and children, and some other characteristic parameters or indicators (e.g cognizable syllable number, the continuity of the language).
Results:
With these quantitative analysis tool and parameters, we can evaluate and analyze the quality of children’s language and cognitive ability objectively and quantitatively to provide the basis for decision-making criteria for parents. Thereby, they can adopt appropriate measures for children to promote the development of children's language and cognitive status.
Conclusion:
In this paper, according to the existing study of children’s language development, we put forward several indicators in the process of automatic measurement for language development which influence the formation of children’s language. From the experimental results we can see that after the pretreatment (including signal enhancement, speech activity detection), both divergence algorithm calculation results and the later words count are quite satisfactory compared with the actual situation.
