Acoustic Identification of Sentence Accent in Speakers with Dysarthria: Cross-Population Validation and Severity Related Patterns

Abstract

Dysprosody is a hallmark of dysarthria, which can affect the intelligibility and naturalness of speech. This includes sentence accent, which helps to draw listeners’ attention to important information in the message. Although some studies have investigated this feature, we currently lack properly validated automated procedures that can distinguish between subtle performance differences observed across speakers with dysarthria. This study aims for cross-population validation of a set of acoustic features that have previously been shown to correlate with sentence accent. In addition, the impact of dysarthria severity levels on sentence accent production is investigated. Two groups of adults were analysed (Dutch and English speakers). Fifty-eight participants with dysarthria and 30 healthy control participants (HCP) produced sentences with varying accent positions. All speech samples were evaluated perceptually and analysed acoustically with an algorithm that extracts ten meaningful prosodic features and allows a classification between accented and unaccented syllables based on a linear combination of these parameters. The data were statistically analysed using discriminant analysis. Within the Dutch and English dysarthric population, the algorithm correctly identified 82.8 and 91.9% of the accented target syllables, respectively, indicating that the capacity to discriminate between accented and unaccented syllables in a sentence is consistent with perceptual impressions. Moreover, different strategies for accent production across dysarthria severity levels could be demonstrated, which is an important step toward a better understanding of the nature of the deficit and the automatic classification of dysarthria severity using prosodic features.

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

“…Paralinguistically, changes in speech prosody, such as a monotonous tone, reduced pitch variation, and altered speech rate, can indicate emotional distress or neurological disorders [60]. Further, the detection of micro-expressions, or subtle facial movements, alongside analysis of gesture frequency and congruence with verbal output, can provide additional context to the emotional state and cognitive functioning of an individual.…”