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Model Identifikasi Kata Ucapan Tuna Wicara
https://doi.org/10.22146/ijeis.47609 Nuruddin Wiranda(1*), Agfianto Eko Putro(2)
(1) Program Studi Pendidikan Ilmu Komputer, FKIP, ULM, Banjarmasin
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA, UGM, Yogyakarta
(*) Corresponding Author
Abstract
Speech impaired is the inability of someone to speak, even though speaking ability is important in order to communicate with other people. Dealing with this as someone who has speech impairments has their own way of communicating, namely by using sign language, but not everyone understands the sign language. The MFCC and Backpropagation ANN methods are implemented on a Single Board Computer (SBC) designed to overcome speech impaired communication problems. The MFCC method is used to retrieve the features of speech impairment and the Backpropagation ANN is used for sound pattern recognition.
The system was trained using 750 sound samples consisting of 5 speakers, each uttering as many as 30 repetitions of the pronunciation of words (makan, kamar, kerja, harga and lapar), then tested using 125 sound samples consisting of 5 speakers, each saying 5 repetitions of words. Training and testing of Backpropagation ANN using input coefficients generated from MFCC. The results showed that the MFCC and Backpropagation ANN methods were able to identify speech words with 60% accuracy, 40% precision and 40% sensitivity.
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[1] Hikmat, R. H., “Booklet Kementerian Sosial, Badan Pendidikan dan Penelitian Kesejahteraan Sosial Pusat Data dan Informasi Kesejahteraan Sosial Jakarta,” halaman 54. 2012 [Online].Available:http://perpustakaan.bappenas.go.id/lontar/file?file=digital/143165-%5B_Konten_%5D-Konten%20D72.pdf [Accessed: 13-Juli- 2019].
[2] Chandra, E., Manikandan, K., dan Sivasankar, M., “A Proportional Study On Feature Extraction Method In Automatic Speech Recognition System,” International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 2, Issue 1 , January 2014, ISSN(Online) : 2321 – 2004, ISSN(Print) : 2321 – 5526. [Online].Available:https://ijireeice.com/wp-content/uploads/2013/03/IJIREEICE5C_s_siva_A_proportional.pdf [Accessed: 13-Juli- 2019].
[3] Srinivasan, V., Ramalingam, V., dan Arulmozhi, P., “Artificial Neural Network Based Pathological Voice Classification Using MFCC Features,” International Journal Of Science, Environment And Technology, Vol. 3, No 1, 2014, 291 – 302, ISSN 2278-3687. [Online]. Available:https://pdfs.semanticscholar.org/241b/313fd5758095d74abe8da7b8aa22e2348075.pdf [Accessed: 13-Juli- 2019].
[4] Chauhan, H.B. and Tanawala, B.A., “Comparative study of MFCC and LPC algorithms for Gujrati isolated word recognition,” International Journal of Innovative Research in Computer and Communication Engineering, 3(2), pp.822-826. 2015 [Online]. Available: https://pdfs.semanticscholar.org/7c26/a9a579e14a1034eee909a2f34f12f5e44ae3.pdf [Accessed: 13-Juli- 2019].
[5] Desai N. , Dhameliya Kinnal., dan Desai Vijayendra., “Feature Extraction and Classification Techniques for Speech Recognition: A Review,” International Journal of Emerging Technology and Advanced Engineering, Volume 3, Issue 12, December 2013, ISSN 2250-2459, ISO 9001:2008 Certified Journal. 2013 [Online]. Available:https://ijireeice.com/wp-content/uploads/2013/03/IJIREEICE5C_s_siva_A_proportional.pdf [Accessed: 13-Juli- 2019].
[6] Wang, Y. and Lawlor, B., “Speaker recognition based on MFCC and BP neural networks,” In 2017 28th Irish Signals and Systems Conference (ISSC) (pp. 1-4). IEEE. 2017 [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7983644 [Accessed: 13-Juli- 2019].
[7] Asda, T.M.H., Gunawan, T.S., Kartiwi, M. and Mansor, H., “Development of Quran reciter identification system using MFCC and neural network,” Indonesian Journal of Electrical Engineering and Computer Science, 1(1), pp.168-175. 2016 [Online]. Available: https://www.researchgate.net/profile/Teddy_Gunawan/publication/299433726_Development_of_Quran_Reciter_Identification_System_Using_MFCC_and_Neural_Network/links/5919b70b0f7e9b1db6527de1/Development-of-Quran-Reciter-Identification-System-Using-MFCC-and-Neural-Network.pdf [Accessed: 13-Juli- 2019].
[8] Srivastava, N., “Speech recognition using artificial neural network,” International Journal of Engineering Science and Innovative Technology (IJESIT), 3(3), pp.406-408. 2014 [Online]. Available:https://pdfs.semanticscholar.org/5f0c/0fa960bb59294c8351a315824d3582735ab5.pdf [Accessed: 13-Juli- 2019].
[9] Sivan, S. dan Gopakumar, C., “An MFCC Based Speaker Recognition using ANN with Improved Recognition Rate,” International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS). 2014 [Online]. Available:http://iasir.net/IJETCASpapers/IJETCAS14-426.pdf [Accessed: 13-Juli- 2019].
[10] Sharma S., Shukla A., Mishra Pankaj, “Speech and Language Recognition using MFCC and DELTA-MFCC,” International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 9 - Jun 2014. [Online]. Available:http://ijettjournal.org/archive/ijett-v12p286[Accessed: 13-Juli- 2019].
DOI: https://doi.org/10.22146/ijeis.47609
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