Telepractice in School-age Children Who Stutter: A Controlled Before and After Study to Evaluate the Efficacy of MIDA-SP

The COVID-19 pandemic necessitated a general reorganization of rehabilitation services in Italy. The lockdown in Italy led to the use of telepractice for the delivery of speech therapy, including stuttering. The aim of the present work was to evaluate the effectiveness of the MIDA-SP (Tomaiuoli et al., 2012), delivered online for school-age children who stutter. A non-randomized controlled pre- and post-treatment study included an experimental group (11 children) receiving a telepractice adaptation of MIDA-SP and a historical control group (11 children) receiving in-person MIDA-SP. Both groups had been assessed with SSI-4 and OASES-S pre- and post-treatment. No statistically significant differences were found between the two modes of delivery. These findings suggest that MIDA-SP treatment delivered via telepractice is effective for school-age children who stutter

Public attitudes toward stuttering in Europe: within-country and between-country comparisons

Introduction: Epidemiological research methods have been shown to be useful in determining factors that might predict commonly reported negative public attitudes toward stuttering. Previous research has suggested that stuttering attitudes of respondents from North America and Europe (i.e., “The West”), though characterized by stereotypes and potential stigma, are more positive than those from several other regions of the world. This inference assumes that public attitudes within various regions characterized by “The West” are similar. Purpose: This study aimed to determine the extent to which public stuttering attitudes are similar or different both within regions of three different European countries and between or among five different European countries or similar geographic areas. It also aimed to compare these European attitudes to attitudes from 135 samples around the world using a standard measure. Material and methods: Using convenience sampling, 1111 adult respondents from eight different investigations completed the Public Opinion Survey of Human Attributes-Stuttering (POSHA-S) in the dominant language of each country or area. In Study I, the authors compared attitudes within three different regions of Bosnia & Herzegovina, Italy, and Norway. In Study II, the authors compared attitudes between combined samples from Bosnia & Herzegovina, Italy, and Norway (with additional respondents from Sweden), and two other samples, one from Germany and the other from Ireland and England. Results: Attitudes of adults from the three samples within Bosnia & Herzegovina, Italy, and Norway were remarkably similar. By contrast, attitudes between the five different countries or area were quite dramatically different. Demographic variables on the POSHA-S did not predict the rank order of these between-country/area differences. Compared to the POSHA-S worldwide database, European attitudes ranged from less positive than average (i.e., Italians) to more positive than average (i.e., Norwegians and Swedes). Conclusion: Factors related to national identity appear to play a significant role in differences in public attitudes in Europe and should be explored in future research

Acoustic analysis in stuttering: a machine-learning study

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)

Acoustic analysis in stuttering: a machine-learning study

Background: Stuttering 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). Objective: We 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. Methods: Fifty-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). Results: Acoustic 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. Conclusion: Acoustic 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)