Text to Speech Using Finite State Automata on Health Data

Authors

  • Indrianto Institut Teknologi PLN, West Jakarta, Indonesia
  • Abdurrasyid Institut Teknologi PLN
  • Meilia Nur Indah Susanti Institut Teknologi PLN
  • Givari Fairus Ferdiansyah Deu Institut Teknologi PLN
  • Arief Ramadhan Institut Teknologi PLN

DOI:

https://doi.org/10.21776/jeeccis.v17i1.1631

Keywords:

Confusion Matrix, Finite State Automata, Text-to-Speech, visually impaired aids

Abstract

Computer science and engineering has provided many benefits that can be applied in our lives, such as in the field of automata theory, one of the largest areas related to the efficiency of an algorithm in solving problems in computational models. Text to Speech is a technology that converts text into sound using a phonetization system, phonemes that are arranged to form a speech to make computers able to communicate and interact with everyday spoken language. Data that need to be interpret is the health data such as body temperature, heart rate per minute, and oxygen levels. Text to Speech is very useful to be applied to blind aids who need information in the form of sound because of their limitations. For this reason, it is necessary to make an application that can read text-based data that is stored into a voice that can be heard by the blind, the method used in this study is Finite State Automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. in this study, the test was carried out using the Confusion Matrix method, while the results obtained were 97% accurate.

Downloads

Published

2023-03-28

How to Cite

[1]
Indrianto, Abdurrasyid, M. . Nur Indah Susanti, G. . Fairus Ferdiansyah Deu, and A. . Ramadhan, “Text to Speech Using Finite State Automata on Health Data ”, jeeccis, vol. 17, no. 1, pp. pp 1–7, Mar. 2023.

Issue

Section

Articles