Exploring Speech and Language Therapists’ Perspectives of Voice-Assisted Technology as a Tool for Dysarthria: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: People living with Parkinson disease (PD) often experience low speech volume and reduced intelligibility. Research suggests that common voice-assisted technology (VAT) devices, like Amazon Alexa and Google Home, can encourage individuals to modify their speech, speaking more clearly, slowly, and loudly. This highlights the potential of VAT as a therapeutic clinical tool in speech and language therapy (SLT). However, while VAT is emerging as a novel health care technology, gaps exist regarding understanding speech and language therapists' (SaLTs) experiences using these devices in clinical practice for PD-related speech and voice difficulties. OBJECTIVE: This research set out to explore various experiences of using VAT to address hypokinetic dysarthria, secondary to PD, from a range of stakeholder perspectives. This paper specifically focuses on clinical insights from SaLTs. METHODS: SaLTs with prior experience of using smart speakers in clinical practice with people with speech or voice difficulties were invited to participate in focus groups or interviews. Between September and December 2024, seven SaLTs participated in semistructured focus groups or interviews using a topic guide. Discussions were informed by published evidence. Results were transcribed and analyzed using a framework analysis approach and were managed through NVivo software (Lumivero). RESULTS: Four main themes were identified across the groups: (1) potential for VAT in SLT, (2) managing therapeutic beige flags, (3) empowering SaLTs to become digitally enabled practitioners, and (4) envisioning the future of VAT in SLT. CONCLUSIONS: This study recognizes VAT's potential as a therapeutic tool that may improve volume, clarity, intelligibility of speech, and facilitate at-home practice for people with PD. However, before VAT can be widely implemented, considerations around data privacy, device limitations, and practical integration into clinical care must be addressed. Future research is proposed to design solutions to address usability challenges for both clients and clinicians. Finally, this paper offers key clinical recommendations for the development of a therapeutic VAT tool for speech and voice difficulties in SLT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".