Malay serves as the language of knowledge and is instrumental in educational settings, as exemplified by its significance in the Education Act of 1961. Furthermore, to support the government's initiatives in advancing the quality of education toward achieving global standards, there is a growing demand for innovative pedagogical methods in teaching the Malay language. In this context, numerous researchers have concentrated their efforts on developing speaker-independent systems, which find applications in language training, articulation therapy, and aiding language learners in mastering the intricacies of Malay phonetics, particularly focusing on vowels. Hence, the principal aim of this paper is primarily dedicated to the recognition of intelligently pronounced Malay syllables by distinct male and female groups, employing Neural Network technology. The primary objective of this research paper is to develop a robust system for the accurate recognition of Malay language syllables, which play a pivotal role in the context of the Malay language, widely used as the primary medium of communication in Malaysia. The implementation of this system leverages the Python programming language, known for its versatility and adaptability to various applications. The paper's primary focus lies in the careful observation and analysis of specific syllable components, particularly those involving the pronunciation of /a/, /e/, /i/, /o/, and /u/. These segments of the language pose particular challenges in terms of pronunciation, and the paper seeks to develop a comprehensive solution for their accurate recognition.
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