Automated Text-to-Speech evaluation methods are being scrutinized for their ability to accurately reflect human perception of speech naturalness. Researchers have developed a linguistically grounded annotation schema, breaking down naturalness into 10 distinct perceptual dimensions. This framework allows for a more nuanced understanding of how listeners perceive speech, moving beyond a single, subjective measure of naturalness. By deconstructing naturalness in this way, the study sheds light on the limitations of current evaluation methods, including Mean Opinion Score predictors and Audio Large Language Models1. The findings have significant implications for the development of more sophisticated TTS systems, which must be able to capture the complexities of human speech perception. This matters to practitioners because it highlights the need for more rigorous evaluation methods, which can inform the development of more effective and realistic TTS systems.