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Automatic voice pathology detection is promising for non-invasive screening and early intervention using sound signals. Nevertheless, existing methods are susceptible to covariate shifts due to background noises, human voice variations, and data selection biases leading to severe performance degradation in real-world scenarios. Hence, we propose a non-invasive framework that contrastively learns personalization from sound waves as a pre-train and predicts latent-spaced profile features through semi-supervised learning. It allows all subjects from various distributions (e.g., regionality, gender, age) to benefit from personalized predictions for robust voice pathology in a privacy-fulfilled manner. We extensively evaluate the framework on four real-world respiratory illnesses datasets, including Coswara, COUGHVID, ICBHI and our private dataset - ASound, under multiple covariate shift settings (i.e., cross-dataset), improving up to 4.12% in overall performance.