The interpretation of medical prescriptions through Text-to-Speech (TTS) systems presents persistent challenges due to the inherent complexity, ambiguity, and variability of clinical text. Prescription data often contains abbreviations, dosage irregularities, shorthand expressions, and context-sensitive terminology that standard normalization approaches fail to process effectively. This paper proposes a context-aware input normalization framework designed to enhance prescription interpretation in TTS systems for clinical decision support applications. The framework integrates lexical normalization, domain-specific dictionary expansion, probabilistic language modeling, and contextual embedding strategies to improve the semantic fidelity of spoken medical instructions.

Building upon prior research in text normalization, medical concept processing, and noisy text transformation, the study synthesizes insights from multilingual normalization models, biomedical concept mapping, and statistical machine translation approaches. The proposed framework extends traditional normalization pipelines by incorporating contextual disambiguation layers that dynamically adapt to prescription semantics based on surrounding linguistic and clinical cues. The study further evaluates the implications of integrating such a framework into healthcare environments, emphasizing its potential to reduce medication errors, improve accessibility for visually impaired users, and support clinical decision systems.

Findings suggest that context-aware normalization significantly improves accuracy in prescription interpretation compared to static dictionary-based methods. However, limitations remain in handling highly unstructured prescriptions and rare medical abbreviations. The study concludes that hybrid normalization architectures combining rule-based and data-driven approaches are essential for robust clinical TTS systems.