Recently, we have proposed a general analytical framework, called Neuro-based Concept Detector (NCD), to interpret the deep representations of a DNN. Based on the activation patterns of hidden neurons, this framework highlights the ability of neurons to detect a specific concept related to the final task. Its main strength is to provide an interpretability tool for any type of DNN performing a classification task, whatever the application domain. Thanks to NCD, we have demonstrated the emergence of phonetic features in the classification layers of a CNNbased model for French phone classification. The emergence of this concept, of great interest in the field of clinical phonetics, has been studied considering healthy speech. Applied to Head and Neck Cancers, we have shown that this framework automatically reflects the level of impairment of the phonetic features produced by a patient, which is supported by the strong correlations with perceptual assessments performed by clinical experts. The objective of the work presented here is to validate the proposed framework by confronting it to new populations of patients, but with very different pathologies (neurodegenerative diseases/ Dysarthria and vocal dysfunction/ Dysphonia). The robustness of the approach to the phonetic content variability of read text is also studied.