Jawi character speech-to-text engine using linear predictive and neural network for effective reading

Othman, Z.A.; Razak, Z.; Abdullah, N.A.; Yusoff, M.Y.Z.B. (2009) Jawi character speech-to-text engine using linear predictive and neural network for effective reading. In: 3rd Asia International Conference on Modelling and Simulation , MAY 25-29, 2009, Bundang, INDONESIA.

Full text not available from this repository. (Request a copy)


Jawi is an old version of Malay Language Writing that need to be preserved. Therefore, it is important to develop tools for teaching kids about Jawi characters and Speech-To-Text (STT) application can serve this purpose well. Unlike English, Jawi uses special characters similar to Arabic Characters. However, its pronunciations are in Malay Language. This uniqueness makes STT development a challenging task. In this paper, we investigate the applicability of Linear Predictive Coding to extract important features from voice signal and Neural Network with Backpropagation to classify and recognize spoken words into Jawi Characters. A total of 225 samples of words in Jawi Characters are recorded from speakers with over 95% accuracy. Jawi Characters Speech-To-Text Engine aims to help students to read Jawi document accurately and independently without the need for close monitoring from parents or teachers.

Item Type: Conference or Workshop Item (Paper)
  1. Othman, Z.A.(University of Malaya)
  2. Razak, Z.(University of Malaya)
  3. Abdullah, N.A.(University of Malaya)
  4. Yusoff, M.Y.Z.B.(University of Malaya)
Additional Information: Univ Malaya, Acad Islam Studies, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia
Uncontrolled Keywords: Speech-To-Text (STT); Linear Predictive Coding (LPC); Artificial Neural Network (ANN); Jawi Writing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Mr. Faizal Hamzah
Date Deposited: 23 Nov 2011 09:37
Last Modified: 23 Nov 2011 09:37
URI: http://eprints.um.edu.my/id/eprint/2286

Actions (For repository staff only: Login required)

View Item