Assessing speech intelligibility of pathological speech in sentences and word lists: The contribution of phoneme-level measures

Xue, Wei; Hout, R.W.N.M. van; Cucchiarini, Catia; Strik, Helmer

doi:10.1016/j.jcomdis.2023.106301

Cited by 3 publications

(1 citation statement)

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“…By incorporating these text-related features, dysarthria classification models can capture and analyze the phonetic intricacies of speech, enabling improved understanding and identification of different dysarthria subtypes and severity levels. Such as in [46], [47], and lexical frequency, phonological neighborhood, word class, and lexical familiarity in [48].…”

Section: ) Text-related Featuresmentioning

confidence: 99%

The Detection of Dysarthria Severity Levels Using AI Models: A Review

Al-Ali,

Al-Maadeed,

Saleh

et al. 2024

IEEE Access

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Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

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Section: ) Text-related Featuresmentioning

confidence: 99%