  Automatic pathological speech detection approaches yield promising results in
identifying various pathologies. These approaches are typically designed and
evaluated for phonetically-controlled speech scenarios, where speakers are
prompted to articulate identical phonetic content. While gathering controlled
speech recordings can be laborious, spontaneous speech can be conveniently
acquired as potential patients navigate their daily routines. Further,
spontaneous speech can be valuable in detecting subtle and abstract cues of
pathological speech. Nonetheless, the efficacy of automatic pathological speech
detection for spontaneous speech remains unexplored. This paper analyzes the
influence of speech mode on pathological speech detection approaches, examining
two distinct categories of approaches, i.e., classical machine learning and
deep learning. Results indicate that classical approaches may struggle to
capture pathology-discriminant cues in spontaneous speech. In contrast, deep
learning approaches demonstrate superior performance, managing to extract
additional cues that were previously inaccessible in non-spontaneous speech
