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

自动病理性言语检测方法在识别不同病理情况方面表现出有希望的结果。本文研究了言语模式对于自动病理性言语检测方法的影响，并对传统机器学习和深度学习两类方法进行了分析。结果表明，传统方法在捕获自发言语中的病理鉴别线索方面存在困难，相比之下，深度学习方法表现出更高的性能，能够提取在非自发言语中无法获得的额外线索。