Our voices are more than carriers of words – they are rich, noisy, beautifully imperfect biosensors. Every breath you take and laugh, sigh, or stutter you utter encodes a chord progression of physiology and psychology: from affective mood, to fatigue, calmness or gleeful states. With AI’s help, we can pick up sharp irregularities and when voices go just a little flat before your health does. Moving from classic speaker ID, we will explore how we can turn vocal cords into health scores using modern affective and health-aware AI. Such AI can tell you who you are, how you feel, and how you’re doing: from detecting anxiety, burnout, and cognitive load to depression, all the way to links with yourself, your behaviour, and chronic conditions. We will discuss how we build models that stay robust in the wild—across devices, languages, and accents—while keeping them clinically meaningful. On the technical side, we will move from representation and neural architecture learning for paralinguistics to self-supervised learning at scale, and how large reasoning models change the game for speaker characterisation. On the societal side, we shall tackle the uncomfortable but essential questions: privacy, bias, efficiency, and explainability—just to name a few of the most essential aspects. Expect a tour from lab demos to real-world deployments in everyday devices—highlighting where “Computational Paralinguistics” rock already, and what more it will take to make voice a trusted instrument in the future health orchestra.