Araştırma Makalesi

Classifiers and Image Processıng to Identify Sign Language Phonemes

Cilt: 10 Sayı: 2 31 Aralık 2024
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Classifiers and Image Processıng to Identify Sign Language Phonemes

Öz

One of the most visible symptoms of autism spectrum disorder is difficulty in speech and language. Difficulties in speech and language are generally very different for each child with autism spectrum disorder. Although some children with autism spectrum disorder can speak fluently, others will not be able to speak normally or will be even nonverbal. In all cases, parents try to communicate with, and understand their children’s needs, desires, and emotions. If a child with autism spectrum disorder cannot speak out loud, it is harder to communicate with him/her but there are other non-vocal methods for communication. In this paper, the benefits of teaching American sign language to children with autism spectrum disorder, the difficulties that families and children will experience while doing this, and technological solutions to these difficulties are presented. In parallel with advancements in technology, novel solutions to understand and use sign language have been proposed and these solutions are supposed to help parents who cannot understand sign language. Such solutions typically rely on image processing methods and classification algorithms to recognise sign language. Therefore, in this paper, the performance of various classification algorithms used to classify American Sign Language phonemes is compared. As the results show, when combined with image processing methods, classification algorithms can be used in various technological solutions aiming at helping to identify sign language phonemes.

Anahtar Kelimeler

Kaynakça

  1. Abu Alfeilat, H. A., Hassanat, A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. (2019). Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review. Big Data, 7(4), 221-248. https://doi.org/10.1089/big.2018.0175
  2. Baker-Ramos, L. K. (2017). Gesture and Signing in Support of Expressive Language Development. i.e.: inquiry in education, 9(2), 2. Retrieved from: https://digitalcommons.nl.edu/ie/vol9/iss2/2
  3. Biçek, E. & Almalı, M. N. (2020). A Mobile Application That Allows People Who Do Not Know Sign Language to Teach Hearing-Impaired People by Using Speech-to-Text Procedures. International Journal of Applied Mathematics Electronics and Computers, 8(1), 27-33. https://doi.org/10.18100/ijamec.682806
  4. Boedeker, P., & Kearns, N. T. (2019). Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer. Advances in Methods and Practices in Psychological Science, 2(3), 250-263. https://doi.org/10.1177/2515245919849378
  5. Bonvillian, J. D., & Nelson, K. E. (1976). Sign language acquisition in a mute autistic boy. Journal of Speech & Hearing Disorders, 41(3), 339-347. https://doi.org/10.1044/jshd.4103.339
  6. Bonvillian, J. D., Nelson, K. E., & Rhyne, J. M. (1981). Sign language and autism. Journal of Autism and Developmental Disorders, 11(1), 125-137. https://doi.org/10.1007/BF01531345
  7. Bowman-Smart, H., Gyngell, C., Morgan, A., & Savulescu, J. (2019). The moral case for sign language education. Monash Bioethics Review, 37(3-4), 94-110. https://doi.org/10.1007/s40592-019-00101-0
  8. Brown, R. S. (1978). Why Are Signed Languages Easier to Learn than Spoken Languages? Part Two. Bulletin of the American Academy of Arts and Sciences, 32(3), 25-44.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yazılım Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

3 Aralık 2024

Yayımlanma Tarihi

31 Aralık 2024

Gönderilme Tarihi

9 Eylül 2024

Kabul Tarihi

10 Kasım 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 10 Sayı: 2

Kaynak Göster

APA
Efeoğlu, E., & Tuna, A. (2024). Classifiers and Image Processıng to Identify Sign Language Phonemes. Kirklareli University Journal of Engineering and Science, 10(2), 219-232. https://doi.org/10.34186/klujes.1546178
AMA
1.Efeoğlu E, Tuna A. Classifiers and Image Processıng to Identify Sign Language Phonemes. KLUJES. 2024;10(2):219-232. doi:10.34186/klujes.1546178
Chicago
Efeoğlu, Ebru, ve Ayşe Tuna. 2024. “Classifiers and Image Processıng to Identify Sign Language Phonemes”. Kirklareli University Journal of Engineering and Science 10 (2): 219-32. https://doi.org/10.34186/klujes.1546178.
EndNote
Efeoğlu E, Tuna A (01 Aralık 2024) Classifiers and Image Processıng to Identify Sign Language Phonemes. Kirklareli University Journal of Engineering and Science 10 2 219–232.
IEEE
[1]E. Efeoğlu ve A. Tuna, “Classifiers and Image Processıng to Identify Sign Language Phonemes”, KLUJES, c. 10, sy 2, ss. 219–232, Ara. 2024, doi: 10.34186/klujes.1546178.
ISNAD
Efeoğlu, Ebru - Tuna, Ayşe. “Classifiers and Image Processıng to Identify Sign Language Phonemes”. Kirklareli University Journal of Engineering and Science 10/2 (01 Aralık 2024): 219-232. https://doi.org/10.34186/klujes.1546178.
JAMA
1.Efeoğlu E, Tuna A. Classifiers and Image Processıng to Identify Sign Language Phonemes. KLUJES. 2024;10:219–232.
MLA
Efeoğlu, Ebru, ve Ayşe Tuna. “Classifiers and Image Processıng to Identify Sign Language Phonemes”. Kirklareli University Journal of Engineering and Science, c. 10, sy 2, Aralık 2024, ss. 219-32, doi:10.34186/klujes.1546178.
Vancouver
1.Ebru Efeoğlu, Ayşe Tuna. Classifiers and Image Processıng to Identify Sign Language Phonemes. KLUJES. 01 Aralık 2024;10(2):219-32. doi:10.34186/klujes.1546178