본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
회원가입 로그인
HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색
결과 내 검색 동의어 포함
결과 내 검색 동의어 포함

-

초록보기

배경 및 목적: 본 연구는 최근 5년간 말 · 언어장애 평가 및 진단을 위해 활용된 인공지능의 전반적 연구 동향을 살펴보고 향후 의사소통장애 분야에서의 인공지능 적용에 대한 시사점을 고찰해 보기 위해 시행되었다. 방법: 체계적 문헌고찰 방법에 따라 2016년부터 2021년 8월까지 국외 학술지에 게재된 총 18편의 논문의 질적분석을 실시하고 특성 및 수행력을 중심으로 분석하였다. 결과: 본 연구에서 선정된 18편의 논문은 전반적으로 낮은 비뚤림을 보고하였고 2018년부터 인공지능을 활용한 평가 및 진단 관련 연구가 증가하는 추세를 보였다. 선정된 문헌의 연구 대상 연령대의 경우 주로 아동을 대상으로 실시되었으며, 분석문헌의 대부분은 실험연구를 통해 그것의 실효성을 검증하였다. 평가 및 진단을 위해 추출된 특성은 음향학적 특성이 주로 사용되었고 말하기 과제 및 음소, 단어 수준에서 분석이 이루어졌으며, 인공지능의 수행력 결과는 목적 및 지표에 따라 상이했다. 논의 및 결론: 본 연구에서는 의사소통장애 분야에서 인공지능 기술을 활용하고자 하는 활발한 시도를 확인하였고 실제 임상현장 적용 가능성을 위한 논의가 이루어졌다. 인공지능이 평가 및 진단을 목적으로 활용되기 위해서는 언어병리학 전문가의 적극적 참여를 통해 각 대상군의 말·언어적 특성을 기반으로 하는 평가 과제 적용이 모색되어야 하며 언어병리학 분야 내 축적된 지식이 반영된 양질의 빅데이터 구축이 선행되어야 한다.

권호기사

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 목차
영유아기 언어발달 및 언어장애 연구 동향 : Research trends regarding language development and language impairment of infancy and toddlerhood : from 2012 to 2021 / 2012년부터 2021년까지 홍경훈, 양희재, 오은별, 이루다 p. 445-467
시선추적기를 활용한 학령전기 아동의 실시간 어휘 학습 양상 및 관련 요인 탐색 = Exploring real-time word learning skills and its related factors in preschool children : an eye-tracking study 양윤희, 임동선, 박원정, 백수정, 강민지 p. 468-482

초등 저학년 발달성 난독 아동의 의미지식에 따른 문단글 읽기유창성 특성 및 오류분석 = Text reading fluency error analysis of Korean 1st and 2nd graders with developmental dyslexia considering semantic knowledge 천해인, 유해림, 배소영 p. 483-494
초등 1-6학년 난독증 아동의 음운인식, 빠른 이름대기, 비단어 따라말하기능력 = Phonological awareness, rapid naming, and nonword repetition abilities in children with dyslexia in grade 1 to 6 윤효진, 김보림 p. 495-505

초등 3-6학년 난독증 아동과 읽기이해부진 아동의 언어능력 = Oral language abilities in children with dyslexia and poor comprehension in grades 3–6 윤효진, 박민영 p. 506-517

이야기 유형 및 처리부하 조건에 따른 초등학교 읽기부진 아동의 읽기이해력 및 읽기 처리 과정에 대한 시선추적연구 = An eye-tracking study on reading comprehension and reading processing in children with reading difficulties according to story type and processing load 백수정, 임동선 p. 518-540

형태소 중심 어휘 중재가 읽기장애 초등학생의 어휘력에 미치는 효과 = The effects of vocabulary intervention based on morphological awareness on the vocabulary ability of elementary school students with reading disabilities 김현아, 김자경 p. 541-557
난독증 성인 파닉스 중재 사례 연구 = The effect of phonics intervention for adult with dyslexia : case study 김기주 p. 558-576
AAC 매체 유형 및 메시지 오류 유형에 따른 해석 정확도 및 수용도 연구 = Interpretation accuracy and acceptance according to AAC types and message error type 최지우, 백경랑, 김영태 p. 577-588
신경언어장애군을 위한 스토리텔링 검사 도구 개발 기초 연구 = Preliminary procedures to develop storytelling-based assessment for aging and neurogenic disorders 최수진, 이혜리, 조은하, 임윤섭, 최유미, 한지연, 성지은 p. 589-605

(The) Korean version of the right hemisphere language battery : 한국어판 우뇌언어기능평가 : 번안과 신뢰성 검증 / adaptation and reliability Jihyeon Yun, Jee-Hye Chung, Yeong-Wook Kim, Il-Young Jung p. 606-616

경도인지장애와 알츠하이머형 치매 환자의 단어찾기 행동 산출 비율과 정보전달 능력의 상관 = Correlation between ratios of word-finding behavior and CIU in patients with mild cognitive impairment and dementia of Alzheimer’s type 최현주 p. 617-628
실시간 비대면 동사의미역강화중재(VNeST)가 비유창성 실어증 환자의 단어 인출에 미치는 효과 = Effects of Verb Network Strengthening Treatment (VNeST) using telepractice on word retrieval in Korean-speakers with non-fluent aphasia 김소은, 라은영, 성지은 p. 629-646

말소리장애 하위유형별 오류패턴 특성 = Phonological error patterns in subgroups of speech sound disorders 하승희 p. 647-657
말소리장애 하위 유형을 예측하는 언어 요인 탐색 = Language ability to predict subtypes of speech sound disorder 피민경, 하승희 p. 658-670
후두과긴장의 중증도와 주관적인 음성평가와의 상관 = Correlation between severity of laryngeal hypertension and subjective voice evaluation 김지성 p. 671-677
노화에 따른 일반 성인의 성대진동시작시간 특성 = Voice onset time in healthy young and old adults 신준영, 김예은, 조예림, 이유진, 김주은, 이영미 p. 678-688
인공와우이식 영유아와 부모의 상호작용에서 부모(의) 언어 입력 특성 = Parental linguistic inputs to toddlers with cochlear implants during parent-toddler interaction 이유진, 박희선, 심현섭, 이영미 p. 689-702
의사소통장애의 평가 및 진단에서 인공지능 적용과 성과에 관한 체계적 문헌고찰 = Applications and performances of artificial intelligence in assessment and diagnosis of communication disorders : a systematic review of the literatures 강혜원, 강진경, 이수복, 심현섭 p. 703-722

6-12세 아동의 국어와 수학 학업수행 능력 잠재성장모델 분석 = The Latent Growth Model of the Korean language and mathematics academic performance in children aged 6-12 이은주 p. 723-741

참고문헌 (62건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Aram, D. M., & Hall, N. E. (1989). Longitudinal follow-up of children with preschool communication disorders: treatment implications. School Psychology Review, 18(4), 487-501. 미소장
2 *Barbera, D. S., Huckvale, M., Fleming, V., Upton, E., Coley-Fisher, H., Doogan, C., ... & Crinion, J. (2021). NUVA: a naming utterance verifier for aphasia treatment. Computer Speech & Language, 69, 1-25. 미소장
3 *Benfatto, M. N., Seimyr, G. Ö., Ygge, J., Pansell, T., Rydberg, A., & Jacobson, C. (2016). Screening for dyslexia using eye tracking during reading. PLoS ONE, 11(12), 1-16. 미소장
4 Bhbosale, S., Pujari, V., & Multani, Z. (2020). Advantages and disadvantages of artificial intellegence. Aayushi International Interdisciplinary Research Journal, 77, 227-230. 미소장
5 Davidson, M. (2002). The interpretation of diagnostic tests: a primer for physiotherapists. Australian Journal of Physiotherapy, 48(3), 227-232. 미소장
6 de la Fuente Garcia, S., Ritchie, C., & Luz, S. (2020). Artificial intelligence, speech and language processing approaches to monitoring Alzheimer’s disease: a systematic review. Journal of Alzheimer’s Disease, 78(4), 1547-1574. 미소장
7 Dhillon, H., Chaudhari, P. K., Dhingra, K., Kuo, R. F., Sokhi, R. K., Alam, M. K., & Ahmad, S. (2021). Current applications of artificial intelligence in cleft care: a scoping review. Frontiers in Medicine, 8, 1-14. 미소장
8 Dockrell, J. E. (2001). Assessing language skills in preschool children. Child Psychology and Psychiatry Review, 6(2), 74-85. 미소장
9 *Dubey, A. K., Prasanna, S. M., & Dandapat, S. (2020). Sinusoidal model-based hypernasality detection in cleft palate speech using CVCV sequence. Speech Communication, 124, 1-12. 미소장
10 Eriksen, M. B., & Frandsen, T. F. (2018). The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. Journal of the Medical Library Association, 106(4), 420-431. 미소장
11 Ertmer, D. J. (2011). Assessing speech intelligibility in children with hearing loss: toward revitalizing a valuable clinical tool. Language, Speech, and Hearing Services in Schools, 2(1), 52-58. 미소장
12 *Hammami, I., Salhi, L., & Labidi, S. (2020). Voice pathologies classification and detection using EMD-DWT analysis based on higher order statistic features. IRBM, 41(3), 161-171. 미소장
13 Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510. 미소장
14 *Hu, H. C., Chang, S. Y., Wang, C. H., Li, K. J., & Cho, H. Y. (2021). Deep learning application for vocal fold disease prediction through voice recognition: preliminary development study. Journal of Medical Internet Research, 23(6), e25247. 미소장
15 Jeong, D. (2017). Trend on artificial intelligence technology and its related industry. Korea Institute of Information Technology Magazine, 15(2), 21-28. 미소장
16 Jung, D. H. (2018). Business strategy in the age of artificial intelligence. Korea: Thequest. 미소장
17 Jung, W. S. (2020). Discrimination and bias of artificial intelligence. Human Beings, Environment and Their Future, 25, 55-73. 미소장
18 *Kadiri, S. R., & Alku, P. (2019). Analysis and detection of pathological voice using glottal source features. IEEE Journal of Selected Topics in Signal Processing, 14(2), 367-379. 미소장
19 Kang, J., Kang, H., Lee, S., & Sim, H. (2022). Research trends on the use of artificial intelligence in intervention for communication disorders. Journal of Speech-Language & Hearing Disorders, 31(2), 107-115. 미소장
20 Kanimozhiselvi, C. S., & Santhiya, S. (2021). Communication disorder identification from recorded speech using machine learning assisted mobile application. Proceedings of the 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 789-793. 미소장
21 Kaur, P., Chavan, B. S., Lata, S., Kaur, A., Tinku, S., Arora, Y., & Ratnam, V. (2006). Early intervention in developmental delay. The Indian Journal of Pediatrics, 73(5), 405-408. 미소장
22 *Kaushik, M., Baghel, N., Burget, R., Travieso, C. M., & Dutta, M. K. (2021). SLINet: dysphasia detection in children using deep neural network. Biomedical Signal Processing and Control, 68, 1-18. 미소장
23 *Khan, R. U., Cheng, J. L. A., & Bee, O. Y. (2018). Machine learning and dyslexia: diagnostic and classification system (DCS) for kids with learning disabilities. International Journal of Engineering & Technology, 7(3.18), 97-100. 미소장
24 Khanagar, S. B., Naik, S., Al Kheraif, A. A., Vishwanathaiah, S., Maganur, P. C., Alhazmi, Y., ... & Patil, S. (2021). Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review. Diagnostics, 11(6), 1004-1015. 미소장
25 Kim, J. H. (2020). AI’s strongest classes. Seoul: Maeil Business Newspaper. 미소장
26 Kim, S. Y., Park, J. E., Seo, H. J., Seo, H. S., Son, H. J., Shin, C. M., Lee, Y. J., Jang, B. H., Heo, D. S. (2011). NECA’s guidance for undertaking systematic reviews and meta-analyses for intervention. Seoul: National Evidence-based Healthcare Collaborating Agency. 미소장
27 Kim, S., & Yim, D. (2021). The effects of parent training on narrative interaction for parents with hearing loss who have children with normal hearing: a case study using AI speakers. Journal of Speech-Language & Hearing Disorders, 30(1), 89-101. 미소장
28 *Kourkounakis, T., Hajavi, A., & Etemad, A. (2021). Fluentnet: end-to-end detection of stuttered speech disfluencies with deep learning. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 2986-2999. 미소장
29 Kunze, K. N., Rossi, D. M., White, G. M., Karhade, A. V., Deng, J., Williams, B. T., & Chahla, J. (2021). Diagnostic performance of artificial intelligence for detection of anterior cruciate ligament and meniscus tears: a systematic review. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 37(2), 771-781. 미소장
30 Law, B. M. (2020). AI: A new window into communication disorders?. Retrieved from https://leader.pubs.asha.org/do/10.1044/leader.FTR1.25102020.44/full/?u. 미소장
31 Lee, Y. K., & Lee, S. M. (2011). Clinical trials and accuracy of diagnostic tests. Journal of Genetic Medicine, 8(1), 28-34. 미소장
32 Lee, Y., Park, H., Lee, Y. (2021). A systematic review of parent training for children with communication disorders in Korea. Communication Sciences & Disorders, 26(3), 727-741. 미소장
33 *Li, M., Tang, D., Zeng, J., Zhou, T., Zhu, H., Chen, B., & Zou, X. (2019). An automated assessment framework for atypical prosody and stereotyped idiosyncratic phrases related to autism spectrum disorder. Computer Speech & Language, 56, 80-94. 미소장
34 Liss, J. (2020). How will artificial intelligence reshape speech-Language pathology services and practice in the future?. Retrieved from https://academy.pubs.asha.org/2020/08/how-will-artificial-intelligence-reshape-speech-language-pathology-services-and-practice-in-the-future/. 미소장
35 *Liu, Y., Lee, T., Law, T., & Lee, K. Y. S. (2019). Acoustical assessment of voice disorder with continuous speech using ASR posterior features. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(6), 1047-1059. 미소장
36 *Mahmoud, S. S., Kumar, A., Tang, Y., Li, Y., Gu, X., Fu, J., & Fang, Q. (2020). An efficient deep learning-based method for speech assessment of mandarin-speaking aphasic patients. IEEE Journal of Biomedical and Health Informatics, 24(11), 3191-3202. 미소장
37 *Mathad, V. C., & Prasanna, S. M. (2019). Vowel onset point based screening of misarticulated stops in cleft lip and palate speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 450-460. 미소장
38 Melo, F. S., Sardinha, A., Belo, D., Couto, M., Faria, M., Farias, A., ... & Ventura, R. (2019). Project INSIDE: towards autonomous semi-unstructured human–robot social interaction in autism therapy. Artificial Intelligence in Medicine, 96, 198-216. 미소장
39 Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. PLOS Medicine, 6(7), 1-6. 미소장
40 Moon, J. E., & Cho, Y. J. (2021). Prospects for the development of rehabilitation medical robots. Journal of Digital Convergence, 19(6), 393-398. 미소장
41 *Nakai, Y., Takiguchi, T., Matsui, G., Yamaoka, N., & Takada, S. (2017). Detecting abnormal word utterances in children with autism spectrum disorders: machine-learning-based voice analysis versus speech therapists. Perceptual and Motor Skills, 124(5), 961-973. 미소장
42 Oleson, J. J., Brown, G. D., & McCreery, R. (2019). The evolution of statistical methods in speech, language, and hearing sciences. Journal of Speech, Language, and Hearing Research, 62(3), 498-506. 미소장
43 Park, C., Lim, Y., Sung, J. E. (2019). Machine-learning and corpus-based analyses of Korean nouns from subjects and objects: stimuli development of verb treatment for neurogenic communication disorders. Communication Sciences & Disorders, 24(4), 968-985. 미소장
44 Park, H. J. (2020). Trend analysis of korea papers in the fields of ‘artificial intelligence’, ‘machine learning ‘and ‘deep learning’. The Journal of Korea Institute of Information, Electronics, and Communication Technology, 13(4), 283-292. 미소장
45 *Perera, H., Shiratuddin, M. F., Wong, K. W., & Fullarton, K. (2018). EEG signal analysis of writing and typing between adults with dyslexia and normal controls. International Journal of Interactive Multimedia and Artificial Intelligence, 5(1), 62-68. 미소장
46 Preston, J., & Edwards, M. L. (2010). Phonological awareness and types of sound errors in preschoolers with speech sound disorders. Journal of Speech, Language, and Hearing Research, 53(1), 44-60. 미소장
47 Qin, Y., Lee, T., Kong, A. P. H., & Law, S. P. (2016). Towards automatic assessment of aphasia speech using automatic speech recognition techniques. Proceedings of the 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), 1-4. 미소장
48 *Reddy, M. K., Alku, P., & Rao, K. S. (2020). Detection of specific language impairment in children using glottal source features. IEEE Access, 8, 15273-15279. 미소장
49 Seo, K. W., & Moon, H. K. (2017). Method logical quality of meta-analyses on the multi-culture. Asia-Pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(8), 211-223. 미소장
50 *Sharma, G., Zhang, X. P., Umapathy, K., & Krishnan, S. (2021). Audio texture and age-wise analysis of disordered speech in children having specific language impairment. Biomedical Signal Processing and Control, 66, 1-8. 미소장
51 Shea, B. J., Hamel, C., Wells, G. A., Bouter, L. M., Kristjansson, E., Grimshaw, J., ... & Boers, M. (2009). AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of Clinical Epidemiology, 62(10), 1013-1020. 미소장
52 Shen, J., Zhang, C. J., Jiang, B., Chen, J., Song, J., Liu, Z., ... & Ming, W. K. (2019). Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Medical Informatics, 7(3), 1-15. 미소장
53 Sim, H. S., Kim, Y. T., Lee, Y. K., Kim, M. S., Kim, S. J., Lee, E. J., … & Yoon, M. S. (2019). Diagnosis & evaluation in communication disorders (2nd). Seoul: Hakjisa. 미소장
54 Song, S., Chon, H., Lee, S. B., (2020). Analyzing the disfluency of reading tasks of persons who stutter based on deep learning and word embedding. Communication Sciences & Disorders, 25(3), 721-737. 미소장
55 Voleti, R., Liss, J. M., & Berisha, V. (2019). A review of automated speech and language features for assessment of cognitive and thought disorders. IEEE Journal of Selected Topics in Signal Processing, 14(2), 282-298. 미소장
56 Voss, C., Schwartz, J., Daniels, J., Kline, A., Haber, N., Washington, P., ... & Wall, D. P. (2019). Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: a randomized clinical trial. JAMA Pediatrics, 173(5), 446-454. 미소장
57 *Wang, X., Yang, S., Tang, M., Yin, H., Huang, H., & He, L. (2019). Hypernasality Net: Deep recurrent neural network for automatic hypernasality detection. International Journal of Medical Informatics, 129, 1-12. 미소장
58 Ward, S. (1999). An investigation into the effectiveness of an early intervention method for delayed language development in young children. International Journal of Language & Communication Disorders, 34(3), 243-264. 미소장
59 Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., ... & QUADAS-2 Group*. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529-536. 미소장
60 Wijesinghe, A., Samarasinghe, P., Seneviratne, S., Yogarajah, P., & Pulasinghe, K. (2019). Machine learning based automated speech dialog analysis of autistic children. Proceddigns of the 2019 11th International Conference on Knowledge and Systems Engineering (KSE), 1-5. 미소장
61 Zhang, J., & Mueller, S. T. (2005). A note on ROC analysis and non-parametric estimate of sensitivity. Psychometrika, 70(1), 203-212. 미소장
62 Zhao, Y., Wang, D., Johnson, E. M., & Healy, E. W. (2018). A deep learning-based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions. The Journal of the Acoustical Society of America, 144(3), 1627-1637. 미소장

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차