Charles Explorer logo
🇨🇿

Guidelines for Speech Recording and Acoustic Analyses in Dysarthrias of Movement Disorders

Publikace na 1. lékařská fakulta |
2021

Tento text není v aktuálním jazyce dostupný. Zobrazuje se verze "en".Abstrakt

Most patients with movement disorders have speech impairments resulting from sensorimotor abnormalities that affect phonatory, articulatory, and prosodic speech subsystems. There is widespread cross-discipline use of speech recordings for diagnostic and research purposes, despite which there are no specific guidelines for a standardized method.

This review aims to combine the specific clinical presentations of patients with movement disorders, existing acoustic assessment protocols, and technological advances in capturing speech to provide a basis for future research in this field and to improve the consistency of clinical assessments. We considered 3 areas: the recording environment (room, seating, background noise), the recording process (instrumentation, vocal tasks, elicitation of speech samples), and the acoustic outcome data.

Four vocal tasks, namely, sustained vowel, sequential and alternating motion rates, reading passage, and monologues, are integral aspects of motor speech assessment. Fourteen acoustic vocal speech features, including their hypothesized pathomechanisms with regard to typical occurrences in hypokinetic or hyperkinetic dysarthria, are hereby recommended for quantitative exploratory analysis.

Using these acoustic features and experimental speech data, we demonstrated that the hyperkinetic dysarthria group had more affected speech dimensions compared with the healthy controls than had the hypokinetic speakers. Several contrasting speech patterns between both dysarthrias were also found.

This article is the first attempt to provide initial recommendations for a standardized way of recording the voice and speech of patients with hypokinetic or hyperkinetic dysarthria; thus allowing clinicians and researchers to reliably collect, acoustically analyze, and compare vocal data across different centers and patient cohorts.