How EZSpeech Uses Controllable AI to Augment Home Practice
Speech therapy practice using computer vision
From SLPs Talk Tech Live, September 24, 2025 -
Tech founder, Firouza Eshonova, joined us to share why she built EZSpeech. After struggling to find consistent, high-volume practice materials for her son, who has dysarthria, Firouza decided to create a solution. She realized there was a need for a technology tool that could assist with the repetitive, structured practice required for his condition.
EZSpeech uses “controllable AI,” focused on predictable, tailored feedback, specifically designed not to be a generative black box like ChatGPT. This focus on safety and efficacy earned EZSpeech a Bronze award at UCSF-Stanford’s 2025 Pediatric Device Accelerator Pitch Competition.
What is Controllable AI? This is a term used to describe AI systems engineered for predictable, reliable, and non-generative outputs. Tools that use controllable AI are intentionally constrained, ensuring feedback aligns with interventions and protocols, unlike open-ended generative models.
From Video to AI feedback
EZSpeech is built on Video Self-Monitoring (VSM), an observational learning technique where an individual watches a video of themselves performing a target behavior or skill, to teach new skills or provide feedback.
It uses computer vision to track a user’s performance and provides immediate, specific feedback tailored to the error made. Computer vision AI is where a computer can “see” visual data and “understand” what is seen based on prior training data, to accurately track movements and actions.
Key design components:
1. Built-in Feedback Mechanisms
The platform offers over 500 exercises, and the majority of the content focuses on: speech and language development, literacy, and social/living skills.
Language Development: Combining two words, sentence formulation, prepositions, opposites, and age-related topics.
Literacy: Storytelling/sequencing tasks.
Social & Living Skills: Practicing social scenarios (e.g., playground rules) and sequencing functional activities (e.g., brushing teeth).
2. Personalized & Achievable Targets
EZSpeech has developed an algorithm that finds an achievable performance level for their users. This personalized target, which gradually increases, ensures the feedback is supportive rather than discouraging. The content can also be personalized to increase engagement.
Key Takeaways:
EZSpeech, a technology developed by a parent, Firouza Eshonova, who struggled to find effective home practice materials for her son with dysarthria. EZSpeech addresses this gap by utilizing Controllable AI and Computer Vision within a Video Self-Monitoring (VSM) framework to deliver structured, high-volume practice. The platform’s design focuses on personalized and achievable targets and a wide array of exercises (speech/language, literacy, social skills). Ultimately, EZSpeech serves as an augmentation tool, automating repetitive training while relying on the SLP for clinical judgment, goal setting, and interpretation of the quantitative data.
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What’s Next:
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