A Qualitative Analysis of Clinician Perspectives of Ultrasound Biofeedback for Speech Sound Disorders.

PURPOSE: Ultrasound biofeedback therapy (UBT) is a relatively new type of technology-assisted speech-language therapy and has shown promise in remediating speech sound disorders. However, there is a current lack of understanding of the barriers and benefits that may influence the usage behavior and clinical decision making for the implementation of UBT from a clinician perspective. In this qualitative study, we explore the perspectives of speech-language pathologists (SLPs) who have used ultrasound biofeedback in programs of speech sound therapy using the unified theory of acceptance and use of technology (UTAUT) model. METHOD: Seven SLPs who had clinical experience treating speech sound disorders with UBT participated. Semistructured in-depth interviews were conducted and video-recorded. Two coders coded and categorized the transcribed data, with consensus established with a third coder. Using thematic analysis, the data were exploratorily grouped into themes along components of the UTAUT model. RESULTS: The highest number of codes was sorted into the "effort expectancy" theme, followed by "performance expectancy," "social influence," and "facilitating conditions" themes of the UTAUT model. Clinicians identified multiple perceived barriers and benefits to the use of ultrasound technology. The top identified barrier was limited accessibility, and the top benefit was the ability to visualize a client's articulatory response to cues on a display. CONCLUSIONS: Clinicians prioritized "effort expectancy" and "performance expectancy" when reflecting on the use of ultrasound biofeedback for speech sound disorders. Clinicians spoke favorably about using UBT for speech sound disorder treatment but acknowledged institutional barriers and limitations at organizational and social levels.

Classification of accurate and misarticulated /ɑr/ for ultrasound biofeedback using tongue part displacement trajectories.

Ultrasound biofeedback therapy (UBT), which incorporates real-time imaging of tongue articulation, has demonstrated generally positive speech remediation outcomes for individuals with residual speech sound disorder (RSSD). However, UBT requires high attentional demands and may therefore benefit from a simplified display of articulation targets that are easily interpretable and can be compared to real-time articulation. Identifying such targets requires automatic quantification and analysis of movement features relevant to accurate speech production. Our image-analysis program TonguePART automatically quantifies tongue movement as tongue part displacement trajectories from midsagittal ultrasound videos of the tongue, with real-time capability. The present study uses such displacement trajectories to compare accurate and misarticulated American-English rhotic /ɑr/ productions from 40 children, with degree of accuracy determined by auditory perceptual ratings. To identify relevant features of accurate articulation, support vector machine (SVM) classifiers were trained and evaluated on several candidate data representations. Classification accuracy was up to 85%, indicating that quantification of tongue part displacement trajectories captured tongue articulation characteristics that distinguish accurate from misarticulated production of /ɑr/. Regression models for perceptual ratings were also compared. The simplest data representation that retained high predictive ability, demonstrated by high classification accuracy and strong correlation between observed and predicted ratings, was displacements at the midpoint of /r/ relative to /ɑ/ for the tongue dorsum and blade. This indicates that movements of the dorsum and blade are especially relevant to accurate production of /r/, suggesting that a predictive parameter and biofeedback target based on this data representation may be usable for simplified UBT.