Rubrics are a commonly used tool for labeling voice corpora in speech quality
assessment, although their application in the context of pathological speech
remains relatively limited. In this study, we introduce a comprehensive rubric
based on various dimensions of speech quality, including phonetics, fluency,
and prosody. The objective is to establish standardized criteria for
identifying errors within the speech of individuals with Down syndrome, thereby
enabling the development of automated assessment systems. To achieve this
objective, we utilized the Prautocal corpus. To assess the quality of
annotations using our rubric, two experiments were conducted, focusing on
phonetics and fluency. For phonetic evaluation, we employed the Goodness of
Pronunciation (GoP) metric, utilizing automatic segmentation systems and
correlating the results with evaluations conducted by a specialized speech
therapist. While the obtained correlation values were not notably high, a
positive trend was observed. In terms of fluency assessment, deep learning
models like wav2vec were used to extract audio features, and we employed an SVM
classifier trained on a corpus focused on identifying fluency issues to
categorize Prautocal corpus samples. The outcomes highlight the complexities of
evaluating such phenomena, with variability depending on the specific type of
disfluency detected.

我们在本研究中介绍了一种基于语音质量不同维度（包括音素、流利度和韵律）的综合性评分标准。通过利用 Prautocal 语料库，我们进行了两个实验，分别对音素和流利度进行评估。结果显示，评估这些现象的复杂性取决于检测到的特定类型的不流畅性，但我们观察到了积极的趋势。

一个综合的标注病理语音的准则

A Comprehensive Rubric for Annotating Pathological Speech

Integration of speech into healthcare has intensified privacy concerns due to
its potential as a non-invasive biomarker containing individual biometric
information. In response, speaker anonymization aims to conceal personally
identifiable information while retaining crucial linguistic content. However,
the application of anonymization techniques to pathological speech, a critical
area where privacy is especially vital, has not been extensively examined. This
study investigates anonymization's impact on pathological speech across over
2,700 speakers from multiple German institutions, focusing on privacy,
pathological utility, and demographic fairness. We explore both training-based
and signal processing-based anonymization methods, and document substantial
privacy improvements across disorders-evidenced by equal error rate increases
up to 1933%, with minimal overall impact on utility. Specific disorders such as
Dysarthria, Dysphonia, and Cleft Lip and Palate experienced minimal utility
changes, while Dysglossia showed slight improvements. Our findings underscore
that the impact of anonymization varies substantially across different
disorders. This necessitates disorder-specific anonymization strategies to
optimally balance privacy with diagnostic utility. Additionally, our fairness
analysis revealed consistent anonymization effects across most of the
demographics. This study demonstrates the effectiveness of anonymization in
pathological speech for enhancing privacy, while also highlighting the
importance of customized approaches to account for inversion attacks.

对匿名化技术在病理性语音中的影响进行了研究，发现它可以增强隐私性，同时强调了为了应对逆向攻击，需要采用定制化方法来平衡隐私和诊断效用。