AI-Based Personalized Slideshow Generator for Daily Media Summaries

Abstract

This paper proposes a well-rounded media consumption system that utilizes speech recognition and generative Artificial Intelligence to create a user-influenced slideshow. The system runs on a Raspberry Pi, using a microphone for input and a touchscreen for output. Spoken user requests are transcribed and categorized into one of five content classes: news, daily activities, social media trends, interest-based topics, and storytelling. These categories are then converted into prompts suitable for image generation. The system uses AI to condense unstructured speech into descriptive, image-ready content, reducing cognitive overhead and minimizing screen time. Unlike conventional browsing, this approach enables passive, voice-controlled consumption of highly relevant media. Qualitative and quantitative evaluation shows that the system reliably transcribes varied speech inputs, classifies user intent with high interpretability, and generates coherent, category-aligned visuals. Specifically, the LLM-based intent classifier achieved 90% accuracy and a Macro-F1 of 0.931 on the tested prompts, while on-device transcription operated at an average CPU utilization of 7.3% with a peak SoC temperature of 48.7°C, confirming the feasibility of the pipeline on a Raspberry Pi. The use of AI in this context enhances personalization, reduces interaction friction, and supports timeefficient engagement with digital media.