AI-Assisted Treatment for Speech Sound Disorders

Overseen By Nina Benway, PhD

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Syracuse University

Must not be taking: Antiepileptics

Disqualifiers: Autism, Down Syndrome, ADHD, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 5 weeks

26 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if artificial intelligence (AI) can help children with speech sound disorders improve their pronunciation of the "r" sound. Participating children will attend weekly online sessions with a speech-language clinician for five weeks. Some will also use an AI-assisted program called Artificial Intelligence-led Speech Motor Chaining (CHAINING-AI) during these weeks, while others will begin using the AI after completing the clinician sessions. The trial seeks school-age children who speak American English, struggle with the "r" sound, and wish to improve it. Participants must have normal hearing and internet access for video calls. As an unphased trial, this study provides a unique opportunity for children to explore innovative AI technology to enhance their speech skills.

Will I have to stop taking my current medications?

The trial information does not specify whether participants need to stop taking their current medications. However, it does exclude those on antiepileptic medication, so it's best to discuss your specific medications with the trial coordinators.

What prior data suggests that this AI-assisted treatment is safe for children with speech sound disorders?

Research has shown that AI-assisted speech therapy is generally safe and well-received. In one trial, children with speech sound issues, such as difficulty with the "r" sound, improved using AI tools, and no safety problems occurred. Caregivers monitored these AI therapy sessions, which were held multiple times, to ensure safety.

For therapy led by speech-language pathologists, studies have found it both effective and safe. It has helped children who did not improve with regular treatments. This method has been used in many trials without any major safety issues.

In summary, both AI and human-led speech therapies have been tested and proven safe in similar situations. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the AI-Assisted Treatment for Speech Sound Disorders because it integrates cutting-edge technology with traditional therapy methods. Unlike standard treatments that rely solely on speech-language pathologists, this method includes an AI-led component that allows for more frequent, flexible practice sessions. The AI component provides 15 additional lessons, enhancing accessibility and possibly accelerating progress, while being supervised by a caregiver. This innovative approach aims to maximize therapy effectiveness by complementing the expertise of human clinicians with the consistency and availability of AI technology.

What evidence suggests that this trial's treatments could be effective for speech sound disorders?

Research has shown that artificial intelligence (AI) in speech therapy can improve speech sound disorders, particularly with the "r" sound. One study found that participants improved their pronunciation of the "r" sound in unpracticed words after AI-assisted treatment. In this trial, participants will receive either a SEQUENTIAL treatment order, starting with sessions with a Speech-Language Pathologist followed by AI-assisted sessions, or a CONCURRENT treatment order, where both types of sessions occur simultaneously. Both treatments aim to enhance children's speech by combining human expertise with new AI tools. These findings suggest that AI can complement traditional methods to achieve better results in speech therapy. ^{1 2 6 7 8}

Are You a Good Fit for This Trial?

This trial is for children aged 9 to 17 with speech sound disorders affecting their 'r' sounds, who speak American English and started learning it by age 3. They must have normal hearing, no significant cognitive or neurological conditions, no obstructive orthodontic appliances, and access to videoconferencing over broadband internet.

Inclusion Criteria

Must receive a percentile score of 8 or below on the Goldman-Fristoe Test of Articulation-3 (GFTA-3) Sounds in Words subtest

Must have access to broadband internet with videoconferencing capabilities

I want to improve how I pronounce the 'r' sound.

Exclusion Criteria

Must have no known history of autism spectrum disorder, Down Syndrome, cerebral palsy, intellectual disability, permanent hearing loss, epilepsy/antiepileptic medication, or brain injury/neurosurgery/stroke

Must have no orthodontic appliances that block the roof of the mouth (e.g., palate expanders)

Must not demonstrate childhood apraxia of speech (CAS-only) features in BOTH articulatory and rate/prosody domains of the ProCAD

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants receive 5 speech lessons with a human speech-language clinician, 1 time per week for 5 weeks. Concurrently or sequentially, they receive 15 speech lessons with an AI clinician, 3 times per week for 5 weeks.

5 weeks

Follow-up

Participants are monitored for changes in speech sound accuracy and retention after treatment

10 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Artificial Intelligence-led Speech Motor Chaining (CHAINING-AI)
• Speech-Language Pathologist-led Speech Motor Chaining

Trial Overview

The study compares two methods of improving the 'r' sound in children: traditional lessons from a human speech-language pathologist once a week and additional sessions three times a week with an AI clinician either concurrently or sequentially.

How Is the Trial Designed?

Group I: SEQUENTIAL treatment order Experimental Treatment 2 Interventions

* 5 speech lessons with a human speech-language clinician: 1 time per week for 5 weeks. * 15 speech lessons with an AI clinician (supervised by the caregiver), 3 times per week for the 5 weeks AFTER the human clinician sessions end.

Group II: CONCURRENT treatment order Experimental Treatment 2 Interventions

* 5 speech lessons with a human speech-language clinician: 1 time per week for 5 weeks. * 15 speech lessons with an AI clinician (supervised by the caregiver), 3 times per week DURING the same 5 weeks as the human clinician sessions.

Artificial Intelligence-led Speech Motor Chaining (CHAINING-AI) is already approved in United States for the following indications:

Approved in United States as ChainingAI for:

• Residual speech sound disorders, specifically for improving the 'r' sound in school-age children

Who Is Running the Clinical Trial?

Syracuse University

Lead Sponsor

Trials

54

Recruited

118,000+

National Institute on Deafness and Other Communication Disorders (NIDCD)

Collaborator

Trials

377

Recruited

190,000+

State University of New York - Upstate Medical University

Collaborator

Trials

176

Recruited

27,600+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Published Research Related to This Trial

Machine learning algorithms (MLAs) can accurately classify patients with Parkinson's disease (PD) and non-PD patients based on voice features, achieving over 90% accuracy.

MLAs can enhance speech rehabilitation for PD patients by converting unclear speech into intelligible speech and automatically assessing speech features, potentially offering a more effective treatment option than traditional therapies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Management of Parkinson's Disease Dysarthria: Can Artificial Intelligence Provide the Solution? Kumar, R., Tripathy, M., Kumar, N., et al.[2022]

The multimodal speech capture system (MSCS) provides real-time and offline visualization of tongue and lip movements along with voice, offering immediate feedback to patients and allowing speech-language pathologists (SLPs) to analyze articulation more effectively.

This innovative system aims to enhance speech rehabilitation by delivering objective, quantitative feedback on speech performance, which could lead to more consistent and effective treatment compared to traditional subjective methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multimodal Speech Capture System for Speech Rehabilitation and Learning. Sebkhi, N., Desai, D., Islam, M., et al.[2018]

The study found that somatosensory inputs to oro-facial structures significantly improved speech processing for low-frequency words, indicating that these sensory cues can enhance lexical access and speech production accuracy.

In contrast, stimulation applied to non-speech areas (forehead) did not produce any significant effects, reinforcing the idea that targeted somatosensory interventions can effectively influence motor speech treatment outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Cross-Modal Somatosensory Repetition Priming and Speech Processing. Namasivayam, AK., Yan, T., Bali, R., et al.[2022]

Machine learning techniques, particularly Light Gradient Boosting, can accurately detect Parkinson's Disease using just voice recordings, achieving a high accuracy rate (AUC of 0.951) with only seven acoustic features.

This study analyzed data from 80 subjects, including 40 patients with Parkinson's Disease and 40 controls, demonstrating that non-invasive voice analysis could serve as an effective screening tool for early detection of the disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Gradient boosting for Parkinson's disease diagnosis from voice recordings. Karabayir, I., Goldman, SM., Pappu, S., et al.[2021]

Advancements in computerized automatic speech recognition (ASR) technology have made it more reliable and user-friendly, expanding its applications beyond simple transcription to various fields, including voice and speech therapy.

ASR technology is expected to transform human-computer interaction by replacing traditional input devices like keyboards and mice with voice commands, allowing for more accessible communication for individuals with oral communication disorders.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automatic speech recognition (ASR) and its use as a tool for assessment or therapy of voice, speech, and language disorders. Kitzing, P., Maier, A., Ahlander, VL.[2018]

Ultrasound biofeedback therapy (UBT) shows promise in improving speech for children with residual speech sound disorder (RSSD), but it can be complex and demanding, necessitating simpler articulation targets for better understanding.

The study developed an image-analysis program, TonguePART, which successfully quantified tongue movements and distinguished accurate from misarticulated /ɑr/ sounds with up to 85% classification accuracy, highlighting the importance of tongue dorsum and blade movements for effective speech production.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Classification of accurate and misarticulated /&#593;r/ for ultrasound biofeedback using tongue part displacement trajectories. Li, SR., Dugan, S., Masterson, J., et al.[2023]

The iPad-based speech therapy app using automatic speech recognition (ASR) showed an 80% agreement with human judgment on speech accuracy, indicating reliable feedback for users.

Participants with apraxia of speech and aphasia demonstrated significant improvements in word production accuracy over a 4-week therapy program, with gains maintained one month after treatment, suggesting the app's effectiveness as a therapeutic tool.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Feasibility of Automatic Speech Recognition for Providing Feedback During Tablet-Based Treatment for Apraxia of Speech Plus Aphasia. Ballard, KJ., Etter, NM., Shen, S., et al.[2020]

Speech Motor Chaining (SMC) is an effective treatment method for school-age children with speech sound disorders, promoting the acquisition of target speech patterns and generalization to untrained words.

Clinicians can implement SMC with over 90% fidelity, allowing children to complete more than 200 trials per session, which supports the method's practicality and potential for positive outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Tutorial: Speech Motor Chaining Treatment for School-Age Children With Speech Sound Disorders. Preston, JL., Leece, MC., Storto, J.[2020]

Citations

1.

surface.syr.edu

surface.syr.edu/etd/1703/

ARTIFICIAL INTELLIGENCE ASSISTED MOTOR ...

These data come from children, adolescents, and young adults aged 6-24 with speech sound disorder and age-matched peers, and have been published ...

2.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC12379609/

Artificial Intelligence–Assisted Speech Therapy for /ɹ

This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelligence ...

3.

clinicaltrials.gov

clinicaltrials.gov/study/NCT05988515

AI-Assisted Treatment for Residual Speech Sound Disorders

The goal of this randomized-controlled trial is to determine how artificial intelligence-assisted home practice may enhance speech learning of the "r" sound ...

4.

ecs.syracuse.edu

ecs.syracuse.edu/about/news/researchers-artificial-intelligence-based-speech-sound-therapy-software-wins-2-5m-nih-grant

Researchers' Artificial Intelligence-Based Speech Sound ...

The project focuses on the most effective scheduling of Speech Motor Chaining sessions for children with speech sound disorders and also ...

5.

pubs.asha.org

pubs.asha.org/doi/abs/10.1044/2024_AJSLP-23-00448

Artificial Intelligence–Assisted Speech Therapy for /ɹ

This study provides evidence of participant improvement for /ɹ/ in untreated words in response to an AI-assisted treatment package.

6.

reporter.nih.gov

reporter.nih.gov/project-details/10796960

Intensive Speech Motor Chaining Treatment and Artificial ...

A randomized controlled trial (n=84) will test the hypothesis that intensive SLP-led Speech Motor Chaining (i.e., bootcamp) leads to greater gains in speech ...

7.

trialx.com

trialx.com/clinical-trials/listings/221995/intensive-speech-motor-chaining-treatment-for-residual-speech-sound-disorders/

Intensive Speech Motor Chaining Treatment for Residual ...

The goal of this randomized-controlled trial is to compare distributed treatment schedules and intensive treatment schedules in 84 school-age ...

8.

speechproductionlab.syr.edu

speechproductionlab.syr.edu/smc-research/

Research with Speech Motor Chaining

Speech Motor Chaining is an evidence-based treatment strategy for treating speech sound errors due to residual speech sound disorders (RSSDs) and childhood ...