Voice-activated diary with text conversion: transforming spoken words into digital memories

Fook, Wilson Wei Sheng (2026) Voice-activated diary with text conversion: transforming spoken words into digital memories. Final Year Project, UTAR.

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Abstract

Many people find it challenging to keep writing in a diary because traditional handwritten diaries are time-consuming and difficult to store. Therefore, this project was created to provide users with a faster, easier, and more modern way to journal by using their voice. Voice Diary is built using Visual Studio Code with the Flutter SDK and Dart language. The key features include voice-to-text conversion, with AssemblyAI used as a speech recognition tool to convert voice messages into text messages that support multiple languages. Mood detection features are included in this mobile app using the DistillBERT Base Uncased GoEmotions Student sentiment analysis library. Other modules, such as tagging, search, calendar integration, multimedia embedding, emotional detection, location and weather detection, theme colour selection, an admin dashboard, PIN protection, and community diary sharing, are implemented to enhance users' experience. The Agile methodology using the Scrum framework is implemented in this project to guide the iterative development of the application. Voice Diary allows users to record their diaries using voice, which is then automatically converted into text messages. Users can track their emotional trends over time using mood ratings and graphs. Voice Diary turns the conventional journaling approach into an advanced yet practical and emotionally reflective activity. This is because Voice Diary makes use of voice-based technologies together with sophisticated capabilities, including mood detection and trend analysis, to make journal writing more convenient and reflective. According to the outcomes of user testing, this suggested solution was effective in ensuring high usability and user satisfaction.

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