Implementation of an Indonesian AI-based text-to-speech system for self-student pickup announcements based on natural language processing

Abstract

Artificial intelligence (AI) and natural language processing (NLP) technologies are advancing rapidly, offering substantial assistance in various fields. This study focuses on developing a text-tospeech (TTS) device tailored for elementary schools in Indonesia to autonomously call students during pickup times. Utilizing the Google text-to-speech (gTTS) library and Python, the device operates on a low-specification Mini PC. It integrates with a cloud-based student management information system (MIS) to synchronize student data and announcements. The device automates the student call-out process, reducing the workload of school staff and ensuring clear, accurate pronunciation. Successfully tested, the device demonstrates a practical, cost-effective solution for modernizing student pickup systems. It showcases the potential of AI and NLP in educational environments, emphasizing operational efficiency and technological accessibility.