@article {10.3844/jcssp.2014.316.324,
article_type = {journal},
title = {LOW FOOTPRINT HIGH INTELLIGIBILITY MALAY SPEECH SYNTHESIZER BASED ON STATISTICAL DATA},
author = {Yong, Lau Chee and Swee, Tan Tian},
volume = {10},
number = {2},
year = {2013},
month = {Nov},
pages = {316-324},
doi = {10.3844/jcssp.2014.316.324},
url = {https://thescipub.com/abstract/jcssp.2014.316.324},
abstract = {Speech synthesis plays a pivotal role nowadays. It can be found in various daily applications such as in mobile phones, navigation systems, languages learning software and so on. In this study, a Malay language speech synthesizer was designed using hidden Markov model to improve the performance of current Malay speech synthesizer and also extend Malay speech technology. Statistical parametric method was utilized in this study. The database was constructed to be balanced with all the phonetic sample appeared in Malay language. The results were rated by 48 listeners and obtained a moderate high rating ranging from 3.79 to 4.23 out of 5. The computed Word Error Rate is 7.1%. The total file size is less than 2 Megabytes which means it is suitable to be embedded into daily application. In conclusion, a Malay language speech synthesizer was designed using statistical parametric method with hidden Markov model. The output speech was verified to be good in quality. The file size is small indicates the feasibility to be used in embedded system.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}