Tutorial: Using Visual-Acoustic Biofeedback for Speech Sound Training

Intervention for residual speech errors in adolescents and adults: A systematised review

When speech sound errors persist beyond childhood they are classified as residual speech errors (RSE) and may have detrimental impacts on an individual's social, educational and employment participation. Despite this, individuals who present with RSE are usually not prioritised on large caseloads. The aim of this literature review was to examine what intervention approaches are available in remediating RSE, and how effective are they for adolescents and adults? A systematised review was undertaken. Comprehensive and systematic searching included search of terms across seven databases, forward and reverse citation searching, and key author contact. Thirty articles underwent critical appraisal before data extraction. Inductive thematic analysis was done before completion of a narrative review. Twenty-three (76.6%) of the articles were from the US and most studies involved intervention for 'r' (90%). Intervention approaches for RSE involved traditional articulation therapy, auditory perceptual training, instrumental approaches, and approaches based on principles of motor learning. Twenty-one studies (70%) investigated the use of more than one intervention approach. Measures of intervention efficacy varied between studies; however, any intervention approach tended to be more successful if delivered in a more intensive schedule. A variety of approaches can be used for RSE, but a combination of high intensity, traditional therapy with adjunctive instrumental biofeedback may be most effective, especially with highly motivated individuals. Unfortunately, this usually requires costly equipment and training to implement. More information about the best dosage and intensity intervention for RSE, evaluated for a larger number of phonemes across other languages and dialects is required.

Traditional and Visual-Acoustic Biofeedback Treatment via Telepractice for Residual Speech Sound Disorders Affecting /ɹ/: Pilot study

Purpose

This study aimed to examine the feasibility of telepractice delivery of a treatment package including visual-acoustic biofeedback and motor-based treatment for residual speech sound disorder affecting /ɹ/ in school-age children. The overall study used a single-case randomization design; however, this preliminary report will simply quantify changes in accuracy before and after completion of the treatment package. The present analysis did not differentiate between the relative contributions of biofeedback and motor-based treatments.

Method

Seven children aged 9-14 received speech therapy for /ɹ/ distortions via telepractice. The study design consisted of three phases: baseline (four sessions), treatment (20 sessions), and post-treatment (three sessions). Treatment included two sessions weekly for a duration of 10 weeks. The participants received one motor-based/non-biofeedback session and one visual-acoustic biofeedback session per week. The order of treatment within each week was randomly determined prior to the start of therapy. Overall progress was assessed using untrained listeners' ratings of word probes administered in the baseline and posttreatment phases.

Results

Findings revealed that six of the seven participants showed a clinically significant response to the overall treatment package, although the magnitude of individual responses varied across speech contexts (consonantal and vocalic) and participants.

Conclusion

The present results suggest that a treatment combining visual-acoustic biofeedback and motor-based treatment for residual /ɹ/ errors treatment can be effectively delivered via telepractice. Considerations for technology setup and treatment protocols are provided.

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.