Specific subtype of fluency disorder affecting French speaking children: A phonological analysis

Teitler, Nadia; Ferré, Sandrine; Dailly, Clémentine

doi:10.1016/j.jfludis.2016.09.002

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…As shown by the most relevant acoustic features selected by machine-learning, our methodology fits with previous studies based on spectral analysis, thus confirming the biological plausibility of our observations. It should be mentioned that all previous studies aiming at the objective analysis of stuttering were based on standardized spectral analysis and were able to find multiple abnormal acoustic features in stuttering (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). These investigations have certainly contributed to improve current knowledge of stuttering by reporting specific changes in acoustic features (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26).…”

Section: Machine-learning Analysis In People With Stutteringmentioning

confidence: 99%

“…It should be mentioned that all previous studies aiming at the objective analysis of stuttering were based on standardized spectral analysis and were able to find multiple abnormal acoustic features in stuttering (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). These investigations have certainly contributed to improve current knowledge of stuttering by reporting specific changes in acoustic features (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). However, standard acoustic analysis does not allow for dynamically combining selected features extracted from a large dataset, and it does not offer the opportunity to automatically learn and improve from experience (29,32,33).…”

Section: Machine-learning Analysis In People With Stutteringmentioning

confidence: 99%

“…However, clinical scales are qualitative tools which rely on the examiner's skills and experiences, thus potentially biasing the assessment's accuracy. To overcome these limitations, as an objective tool for assessing stuttering several authors have adopted acoustic analysis and reported several changes in specific features (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Previous acoustic analysis, however, did not allow to detect the severity of stuttering and monitor its progression.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic analysis in stuttering: a machine-learning study

Asci,

Marsili,

Suppa

et al. 2023

Front. Neurol.

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BackgroundStuttering is a childhood-onset neurodevelopmental disorder affecting speech fluency. The diagnosis and clinical management of stuttering is currently based on perceptual examination and clinical scales. Standardized techniques for acoustic analysis have prompted promising results for the objective assessment of dysfluency in people with stuttering (PWS).ObjectiveWe assessed objectively and automatically voice in stuttering, through artificial intelligence (i.e., the support vector machine – SVM classifier). We also investigated the age-related changes affecting voice in stutterers, and verified the relevance of specific speech tasks for the objective and automatic assessment of stuttering.MethodsFifty-three PWS (20 children, 33 younger adults) and 71 age−/gender-matched controls (31 children, 40 younger adults) were recruited. Clinical data were assessed through clinical scales. The voluntary and sustained emission of a vowel and two sentences were recorded through smartphones. Audio samples were analyzed using a dedicated machine-learning algorithm, the SVM to compare PWS and controls, both children and younger adults. The receiver operating characteristic (ROC) curves were calculated for a description of the accuracy, for all comparisons. The likelihood ratio (LR), was calculated for each PWS during all speech tasks, for clinical-instrumental correlations, by using an artificial neural network (ANN).ResultsAcoustic analysis based on machine-learning algorithm objectively and automatically discriminated between the overall cohort of PWS and controls with high accuracy (88%). Also, physiologic ageing crucially influenced stuttering as demonstrated by the high accuracy (92%) of machine-learning analysis when classifying children and younger adults PWS. The diagnostic accuracies achieved by machine-learning analysis were comparable for each speech task. The significant clinical-instrumental correlations between LRs and clinical scales supported the biological plausibility of our findings.ConclusionAcoustic analysis based on artificial intelligence (SVM) represents a reliable tool for the objective and automatic recognition of stuttering and its relationship with physiologic ageing. The accuracy of the automatic classification is high and independent of the speech task. Machine-learning analysis would help clinicians in the objective diagnosis and clinical management of stuttering. The digital collection of audio samples here achieved through smartphones would promote the future application of the technique in a telemedicine context (home environment).

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Section: Machine-learning Analysis In People With Stutteringmentioning

confidence: 99%

Section: Machine-learning Analysis In People With Stutteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Acoustic analysis in stuttering: a machine-learning study

Asci,

Marsili,

Suppa

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

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Variabilität des Stotterns

Natke

Kohmäscher

2020

Stottern

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Word-Final Disfluencies in a School-Age Child: Beneath the Tip of the Iceberg

Eichorn

Donnan

2021

LSHSS

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Purpose Disfluencies associated with stuttering generally occur in the initial position of words. This study reviews data from a school-age child with an atypical stuttering profile consisting predominantly of word-final disfluencies (WFDs). Our primary goals were to identify patterns in overt features of WFDs and to extend our understanding of this clinical profile by focusing on aspects of stuttering that lie beneath the surface. Method Analyses explored the patterns and distributions of the child's observable stuttering behaviors, in addition to his awareness, perceptions, and subjective experience of stuttering. Results Findings indicated that the WFD profile consists of relatively consistent and distinct overt features. We also found that, in many ways, the child perceived the impact of his disfluencies much like other children who stutter, even though his disfluencies manifested in a less common form. Conclusion Findings suggest the need for increased awareness of the varied forms stuttering may take in order to ensure accurate diagnosis, clear communication to clients and parents, and timely access to appropriate intervention.

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Specific subtype of fluency disorder affecting French speaking children: A phonological analysis

Cited by 5 publications

References 18 publications

Acoustic analysis in stuttering: a machine-learning study

Acoustic analysis in stuttering: a machine-learning study

Variabilität des Stotterns

Word-Final Disfluencies in a School-Age Child: Beneath the Tip of the Iceberg

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