Towards automating the Frenchay dysarthria assessment: Can neural phoneme posteriorgrams inform the analysis of dysarthric speech?

Dysarthria is a type of motor speech disorder that reflects abnormalities in motor movements required for speech production. In clinical practice, identifying characteristic signs and symptoms of the neuropathophysiology underlying a dysarthria is vital for diagnosis and management. The gold standard for dysarthria assessment is auditory-perceptual evaluation by a speech and language therapist for differential diagnosis and management decisions. As the process is time-consuming for clinicians, there is growing interest in automatic dysarthria assessment (ADA). Recent approaches to ADA primarily focus on the classification of broad intelligibility or speech severity labels. However, this does not have much clinical utility and the assessment of communication-relevant parameters do not distinguish between dysarthria types and pathomechanisms. Studies on the classification of dysarthria function or clinical test protocol scores focusing on aspects of dysarthric speech production (such as the Frenchay dysarthria assessment (FDA)) are limited. Therefore, this paper focuses on the preliminary steps towards clinically interpretable ADA, including automatic FDA assessment. The phoneme posteriorgram (PPG) is a time-varying categorical distribution over acoustic speech units, and recent work demonstrates interpretable speech pronunciation distance for downstream tasks, e.g. pronunciation reconstruction. This work extends recent advances in posterior-based phoneme research and mispronunciation models to dysarthria assessment, exploring the extent to which dysarthric speech features in the FDA (identified by auditory-perceptual evaluation in clinical practice) are captured by PPG information. To achieve this, FDA aspects are systematically evaluated. The results show that interpretable PPG probability can capture dysarthric speech features that are related to motor system dysfunction. • This paper introduces a framework for the evaluation of interpretable features (based on analysis of dysarthric speech characteristics across relevant Frenchay dysarthria assessment (FDA) aspects) to address the lack of research on the automation of FDA score prediction. • Previous approaches to dysarthria classification have primarily focused on broad scores based on the deviation between control and dysarthric speech, or the classification of intelligibility labels (or intelligibility as an indicator of speech severity). This falls short of what is done in current clinical gold-standard dysarthria assessment, where auditory-perceptual evaluation is routinely conducted to define speech features related to dysfunction of the motor system (and the prosodic consequences). • FDA aspects are systematically evaluated (including manual listening by a speech and language therapist (SLT) as appropriate). A detailed analysis of the auditory perceptual features relevant to FDA scoring in the TORGO have not been previously documented, as well as analysis of the dysarthric speech processes in context of an interpretable feature. As a preliminary step towards clinically interpretable ADA (including automatic FDA assessment), this paper shows that interpretable PPG probability can capture these dysarthric speech features, with the potential utility to classify speech production impairment and a dysarthria profile. • Multiple pre-processing steps are required to process the TORGO phoneme alignment data. The code for this work will be publicly released. Previous studies have used the TORGO phoneme aligned data ( Yue et al., 2022 ). However, to the authors’ knowledge, no publicly available code is available to process this data.