A Comprehensive Rubric for Annotating Pathological Speech

TL;DR

This paper introduces a detailed rubric for annotating pathological speech, aiming to standardize error identification in speech of individuals with Down syndrome and support automated assessment development.

Contribution

It presents a comprehensive speech quality rubric tailored for pathological speech and evaluates its application using phonetic and fluency experiments with automated metrics.

Findings

Positive correlation trends in phonetic evaluation

Deep learning models can classify fluency issues

Variability in disfluency detection depending on type

Abstract

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.