Goal:Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care.Methods:In this work, we present three generic modeling approaches –unguided,semi-guided, andguidedapproaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively.Results:From detailed analysis with three datasets, we find thatguidedmodels are up to 28% more accurate than theunguidedmodels. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets.Conclusions:Thoughguidedmodels outperform other models, they require a better understanding of the environment.