Enhancing Academic Repository Accessibility through Voice Assistant Integration

Digital repositories are vital tools in higher education, providing scholars with convenient access to a wide array of academic materials. However, most current search systems depend on rigid keyword-based queries and Boolean logic, which can be challenging for casual users, those with limited digital literacy, or individuals with disabilities. With advancements in speech recognition and natural language processing (NLP), voice assistants like Apple Siri, Google Assistant, and Amazon Alexa now enable users to interact with digital content through natural speech. This paper introduces a voice-enabled application specifically designed to enhance the search experience in academic repositories. The system leverages speech-to-text (STT) conversion to process spoken input, NLP to interpret user intent and expand queries, and retrieves relevant documents accordingly. A usability study involving 35 university students was conducted to evaluate system performance in terms of accessibility, effectiveness, and user satisfaction. The results revealed high satisfaction across all metrics, affirming that voice-based interfaces significantly improve the accessibility and usability of digital libraries. The paper also discusses current limitations and outlines directions for future research.