Speech-Related Reaction Times of Stutterers and Nonstutterers

Bakker, Klaas; Brutten, Gene J.

doi:10.1044/jshd.5502.295

Cited by 10 publications

(8 citation statements)

References 10 publications

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“…Overall findings of comaparing the mean STI scores of LL and Jaw art iculators for voiced, unvoiced and combined non-sense labiodental sequences revealed weak support for dyscoordination hypothesis which was proposed to be operative in PsWS. This was incongruent with some of the earlier findngs which failed to show any discoordination between the subsystems of PsWS (Archibald & De Nil, 1999;Bakker &Brutten 1990;Max &Gracco, 2005) There was no significant differences for the mean STI scores between the utterance types for PsWS and typical adults. However, it was intriguing to note that the mean STI scores were highly variable in PsWS for unvoiced labiodental non-sense sequences.…”

Section: Discussion:-contrasting

confidence: 56%

“…Following the weak support for laryngeal aberrations underlying stuttering in the perceptual studies, investigations based on reaction time were attempted (Adams & Hayden, 1976;Cross, Shadden, &Luper, 1979;Reich, Till, Go ldsmith, &Prins, 1979). These studies, in their methodology, did not attempt to address laryngeal deviations as the primary precip itating factor for stuttering but these studies corroborated that PsWS have discoordination between laryngeal and articulatory subsystems of speech (Archibald & De Nil, 1999;Bakker &Brutten 1990;Max &Gracco, 2005;Watson & Alfonso, 1983;Yoshioka &Löfqvist, 1981). Venkatagiri (1982) addressed the discoordination between phonation and articulatory subsystems by reporting the reaction time to produce voiced (/z/) and unvoiced fricative (/s/) consonants in PsWS.…”

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confidence: 99%

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Articulatory Stability o f Non - sense Labiodental Sequences in Persons w ith Stuttering.

MaheshBV¹,

JayasreeG²,

Joseph³

2018

IJAR

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Section: Discussion:-contrasting

confidence: 56%

mentioning

confidence: 99%

Articulatory Stability o f Non - sense Labiodental Sequences in Persons w ith Stuttering.

MaheshBV¹,

JayasreeG²,

Joseph³

2018

IJAR

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“…As a group, PWS have been found to have longer vowel and consonant durations than PNS (Di Simoni, 1974;Starkweather & Myers, 1979. PWS were found to have descriptively longer VOT than PNS (Bakker & Brutten, 1990). Namasivayam & van Lieshout (2008) Adults who stutter (AWS) v.…”

Section: Title: How Fluent Is the Fluent Speech Of People Who Stuttermentioning

confidence: 99%

“…Subsequently the use of electroglottographic (EGG) and electromyographic (EMG) data from the lower lip allowed the calculation of physiological response times (as opposed to acoustic response times), with PWS being found to have descriptively longer VOT than PNS (Bakker & Brutten, 1990).…”

Section: Title: How Fluent Is the Fluent Speech Of People Who Stuttermentioning

confidence: 99%

How fluent is the fluent speech of people who stutter? A new approach to measuring kinematics with ultrasound

Heyde

Scobbie

Lickley

et al. 2015

Clinical Linguistics & Phonetics

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We present a new approach to the investigation of dynamic ultrasound tongue imaging (UTI) data, applied here to analyse the subtle aspects of the fluency of people who stutter (PWS). Fluent productions of CV syllables (C = /k/; V = /ɑ, i, ə/) from three PWS and three control speakers (PNS) were analysed for duration and peak velocity relative to articulatory movement towards (onset) and away from (offset) the consonantal closure. The objective was to apply a replicable methodology for kinematic investigation to speech of PWS in order to test Wingate's Fault-Line hypothesis. As was hypothesised, results show comparable onset behaviours for both groups. Regarding offsets, groups differ in peak velocity. Results suggest that PWS do not struggle initiating consonantal closure (onset). In transition from consonantal closure into the vowel, however, groups appear to employ different strategies expressed in increased variation (PNS) versus decreased mean peak velocity (PWS).

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“…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%

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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Speech-Related Reaction Times of Stutterers and Nonstutterers

Cited by 10 publications

References 10 publications

Articulatory Stability o f Non - sense Labiodental Sequences in Persons w ith Stuttering.

Articulatory Stability o f Non - sense Labiodental Sequences in Persons w ith Stuttering.

How fluent is the fluent speech of people who stutter? A new approach to measuring kinematics with ultrasound

Acoustic analysis in stuttering: a machine-learning study

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