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。

自动病理说话检测中的语音模式影响

Impact of Speech Mode in Automatic Pathological Speech Detection

Speech production is a complex phenomenon, wherein the brain orchestrates a
sequence of processes involving thought processing, motor planning, and the
execution of articulatory movements. However, this intricate execution of
various processes is susceptible to influence and disruption by various
neurodegenerative pathological speech disorders, such as Parkinsons' disease,
resulting in dysarthria, apraxia, and other conditions. These disorders lead to
pathological speech characterized by abnormal speech patterns and imprecise
articulation. Diagnosing these speech disorders in clinical settings typically
involves auditory perceptual tests, which are time-consuming, and the diagnosis
can vary among clinicians based on their experiences, biases, and cognitive
load during the diagnosis. Additionally, unlike neurotypical speakers, patients
with speech pathologies or impairments are unable to access various virtual
assistants such as Alexa, Siri, etc. To address these challenges, several
automatic pathological speech detection (PSD) approaches have been proposed.
These approaches aim to provide efficient and accurate detection of speech
disorders, thereby facilitating timely intervention and support for individuals
affected by these conditions. These approaches mainly vary in two aspects: the
input representations utilized and the classifiers employed. Due to the limited
availability of data, the performance of detection remains subpar.
Self-supervised learning (SSL) embeddings, such as wav2vec2, and their
multilingual versions, are being explored as a promising avenue to improve
performance. These embeddings leverage self-supervised learning techniques to
extract rich representations from audio data, thereby offering a potential
solution to address the limitations posed by the scarcity of labeled data.

语音产生是一个复杂的现象，通过思维加工、运动规划和发音运动的执行，大脑协调一系列过程。然而，这些多种过程的复杂执行容易受到各种神经退行性病理性言语障碍的影响和干扰，如帕金森病，导致话语障碍、运动障碍等情况。这些障碍导致异常的言语模式和不清晰的表达。为了解决这些挑战，已提出了几种自动病理性言语检测方法，主要侧重于输入表示和分类器的应用。由于数据有限，检测的性能仍然不甚理想。自我监督学习嵌入，如 wav2vec2 及其多语言版本，被认为是提高性能的有希望途径。这些嵌入利用自我监督学习技术从音频数据中提取丰富的表征，从而为解决标签数据稀缺性所带来的限制提供了潜在的解决方案。