Generalization With AI Navigation Using StaRt

ConditionSpeech Sound Disorder

Trial statusRecruiting

Trial phasePhase 2

Trial typeInterventional

Biological sexAll

Age9-17

SponsorNew York University

About this trial

Speech Sound Disorder (SSD) affects a significant portion of school-aged children, leading to social and emotional challenges that can persist into adolescence and adulthood. The number of productions necessary for a remediated speech sound to generalize to connected speech is challenging to achieve in practice, leading clinicians to call for accessible, reliable resources allowing children to continue therapy outside of direct clinical interactions. Artificial intelligence (AI) tools hold promise as a means to extend direct service delivery in speech-language pathology, but research investigating the topic has been limited. This study (Generalization with AI Navigation using staRt, or GAINS) will measure the effects of a course of AI-mediated home practice intended to promote generalization of gains made through biofeedback treatment in a related study, Visual-acoustic Intervention with Service Delivery In-person and Via Telepractice Trial (VISIT; NCT06517225).

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

• Must be between 9;0 and 17;11 (years;months) old at the time of enrollment.

Must speak English as the dominant or equally dominant language

Must have begun learning English by age 3, per parent report.

Must hear a rhotic dialect of English from at least one speaker in the home if the home language is English.

Disqualifiers

• Must not exhibit voice or fluency disorder of a severity judged likely to interfere with the ability to participate in study activities.

Must not currently have orthodontia that crosses the palate and cannot be removed.

Must not have history of permanent hearing loss.

Must not have an existing diagnosis of developmental disability such as cerebral palsy or Down Syndrome.

Trial design

Design model

Parallel

Treatments tested in this trial

AI-guided maintenance training with visual-acoustic biofeedback
Behavioral
Participants will complete AI-guided practice in the home setting in which they will interact with a web interface of the staRt web-based software for speech practice. The staRt software will present words to practice and show a real-time visual display of the acoustic signal of speech (biofeedback). On select trials, feedback will be provided by a classifier for children's "r" sounds, PERCEPT. Research assistants will join sessions to facilitate but will not provide direct clinical guidance to participants.
Treatment as Usual (TAU)
Behavioral
Participants will return to their regular community-based care. This may consist of treatment in the school setting or by a private SLP; participants who were not previously receiving treatment may return to a no-treatment condition. Data will be collected from parents/guardians to determine the frequency of treatment, if any, received during this phase.

Treatment groups

76 Participants

are divided into 2 treatment groups

Group A: AI-guided maintenance training with visual-acoustic biofeedbackExperimental treatment 2 interventions

Group B: Treatment as usual [TAU]Active comparator 1 intervention

Trial outcomes

Primary outcomes

1. Percentage of "Correct" Ratings by Blinded Untrained Listeners for /r/ Sounds Produced in Word Probes

To assess generalization of treatment gains to untreated words, participants will be assessed with standard probes (30 words \[considered the primary target\], 20 syllables, and 10 sentences containing /r/ in various phonetic contexts). Stimuli in each probe will be presented individually in randomized order with blocking by stimulus type (word, syllable, sentence). Individual words will be isolated from the audio record of each word probe and presented in randomized order for binary rating (correct/incorrect) by 9 untrained listeners who are blind to treatment condition and time point, but will see the written representation of each target word. We will use the proportion of "correct" ratings for each token as our primary measure of perceptually rated accuracy.

Time frame

Immediately before the initiation of GAINS participation and again after the end of GAINS participation (6 weeks later)

Sponsors and contacts

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New York University

Lead sponsor

Syracuse University

Collaborator

Montclair State University

Collaborator

Source ClinicalTrials.gov

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