<![CDATA[This research examines the use of Speech Processing techniques in an Android-based translation app aimed at improving Natural Language Processing (NLP) for translating between Minang and Indonesian. The Minang language, spoken in West Sumatra, Indonesia, features complex linguistic structures, idiomatic phrases, and specialized vocabulary that challenge automated translation systems. These linguistic attributes make it hard for traditional NLP algorithms to accurately translate the language. To overcome these issues, the app incorporates sophisticated Speech Processing methods to accurately recognize and convert spoken Minang into text for NLP analysis. The NLP component then interprets and translates this text into Indonesian while preserving the Minang language's nuances. The app's evaluation shows significant progress in translation accuracy, especially with simple and common phrases. Nonetheless, it still struggles with intricate sentence structures, dialectal differences, and culturally specific terms unique to Minang. The study highlights the need for customized technological solutions that accommodate the intricacies of regional languages like Minang. Such advancements are essential not only for enhancing communication in multilingual contexts but also for the digital preservation and promotion of indigenous languages in a globalized world.]]>
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