Marco Cardia

As multimodal interaction becomes central to digital learning, technical interfaces, such as eBooks and conversational agents, present unique accessibility challenges for users relying on the auditory modality. While modern Text-to-Speech (TTS) systems excel at rendering natural language, they often fail to convey the complex, structural syntax of source code, creating a significant barrier for blind and low-vision programmers. We frame the vocal rendering of source code as a cross-modal translation task, in which spatial and symbolic information visually encoded in code must be mapped into a linear auditory signal. This study explores "Code-to-Speech" (CTS) as a critical component of multimodal technical communication. We present a comparative evaluation of five leading AI-driven TTS engines (including ChatGPT, Gemini, and ElevenLabs) to determine their efficacy in rendering mixed-content (text and code) materials. Adopting a mixed-methods approach, we combine automated linguistic metrics (WER, BLEU, METEOR) with a human-centered qualitative assessment. Our findings reveal significant differences: ChatGPT and Gemini consistently achieved lower error rates, while Copilot omitted critical tokens. However, all systems struggle with essential structural cues such as indentation, pauses, and code comments. We also show that designed prompts improve intelligibility, particularly for novices. Our work establishes a benchmark for CTS and underscores the need for multimodal AI architectures that support inclusive technical interaction.

1. In the domains of Science, Technology, Engineering, and Mathematics (STEM), visual representations are not merely illustrative aids; they are the primary medium for representing complex data, ab...

The quality of water is key for the quality of agrifood sector. Water is used in agriculture for fertigation, for animal husbandry, and in the agrifood processing industry. In the context of the progressive digitalization of this sector, the automatic assessment of the quality of water is thus becoming an important asset. In this work, we present the integration of Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning in the context of water quality assessment aiming at ensuring water safety and the compliance of water regulation. Furthermore, we emphasize the importance of model interpretability by employing SHapley Additive exPlanations (SHAP) to understand the contribution of absorbance at different wavelengths to the predictions. Our approach demonstrates the potential for rapid, accurate, and interpretable assessment of key water quality parameters.