Age group classification to identify the progress of language development based on convolutional neural networks
Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, softwar...
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Veröffentlicht in: | Journal of intelligent & fuzzy systems 2021-01, Vol.40 (4), p.7745-7754 |
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creator | Oh, Byoung-Doo Lee, Yoon-Kyoung Song, Hye-Jeong Kim, Jong-Dae Park, Chan-Young Kim, Yu-Seop |
description | Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. The proposed model shows an average accuracy of about 74.6 %. |
doi_str_mv | 10.3233/JIFS-189594 |
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In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. 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In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. And we analyze the distribution of the results for each sentence of the speakers to predict their age groups and to check the degree of language development. The proposed model shows an average accuracy of about 74.6 %.</description><subject>Age groups</subject><subject>Artificial neural networks</subject><subject>Chronology</subject><subject>Evaluation</subject><subject>Language</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Speech</subject><subject>Speech therapy</subject><issn>1064-1246</issn><issn>1875-8967</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNotkD1PwzAYhC0EEqUw8QcsMaKAP5LYHquKj6JKDMBsvbGdkJLGwXaK-u9JKdPd8NzpdAhdU3LHGef3L6vHt4xKVaj8BM2oFEUmVSlOJ0_KPKMsL8_RRYwbQqgoGJmhYdE43AQ_Dth0EGNbtwZS63ucPG6t61Nb73H6dHgIvgkuRuxr3EHfjDAlrdu5zg_bicMVRGfxlDS-3_luPLRAh3s3hj9JPz58xUt0VkMX3dW_ztHH48P78jlbvz6tlot1ZlhJU2ZrWTELhiliuJHESU6EEdbRHKzMbUUNA6YYA8nzHMCUnBSlAFCKm0oUfI5ujr3T7u_RxaQ3fgzToKhZQakSXHE-UbdHygQfY3C1HkK7hbDXlOjDpfpwqT5eyn8Bdphrow</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Oh, Byoung-Doo</creator><creator>Lee, Yoon-Kyoung</creator><creator>Song, Hye-Jeong</creator><creator>Kim, Jong-Dae</creator><creator>Park, Chan-Young</creator><creator>Kim, Yu-Seop</creator><general>IOS Press BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210101</creationdate><title>Age group classification to identify the progress of language development based on convolutional neural networks</title><author>Oh, Byoung-Doo ; Lee, Yoon-Kyoung ; Song, Hye-Jeong ; Kim, Jong-Dae ; Park, Chan-Young ; Kim, Yu-Seop</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-df8b2dac290c3c80e8307c7de14ad84db1c2a2922a8344aac630567aa993cb753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age groups</topic><topic>Artificial neural networks</topic><topic>Chronology</topic><topic>Evaluation</topic><topic>Language</topic><topic>Machine learning</topic><topic>Model accuracy</topic><topic>Speech</topic><topic>Speech therapy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oh, Byoung-Doo</creatorcontrib><creatorcontrib>Lee, Yoon-Kyoung</creatorcontrib><creatorcontrib>Song, Hye-Jeong</creatorcontrib><creatorcontrib>Kim, Jong-Dae</creatorcontrib><creatorcontrib>Park, Chan-Young</creatorcontrib><creatorcontrib>Kim, Yu-Seop</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of intelligent & fuzzy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oh, Byoung-Doo</au><au>Lee, Yoon-Kyoung</au><au>Song, Hye-Jeong</au><au>Kim, Jong-Dae</au><au>Park, Chan-Young</au><au>Kim, Yu-Seop</au><au>Hsieh, Wen-Hsiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Age group classification to identify the progress of language development based on convolutional neural networks</atitle><jtitle>Journal of intelligent & fuzzy systems</jtitle><date>2021-01-01</date><risdate>2021</risdate><volume>40</volume><issue>4</issue><spage>7745</spage><epage>7754</epage><pages>7745-7754</pages><issn>1064-1246</issn><eissn>1875-8967</eissn><abstract>Speech pathology is a scientific study of speech disorders. In this field, the study also analyzes and evaluates language abilities for the purpose of improving speech and hearing. Speech therapy first performs evaluation of speech ability, which is expensive. In order to solve this problem, software methodologies have been applied to language analysis, but most of them have been applied to only part of the whole process. In this study, the degree of language development is judged by determining the age group of the speaker (Pre-school children, Elementary school, Middle and high school, Adults, and Senior citizen) using deep learning and simple statistics. We use transcription data from the counseling contents and multi-kernel CNN model. At this time, in order to understand the characteristics of Korean language belonging agglutinative languages, experiments are carried out in words, morphemes, characters, Jamo, and Jamo with POS tag-level. 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subjects | Age groups Artificial neural networks Chronology Evaluation Language Machine learning Model accuracy Speech Speech therapy |
title | Age group classification to identify the progress of language development based on convolutional neural networks |
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