Research has shown that early diagnosing and intervention of speech impairments can lead to major long-term positive effects for the individual later in life. As such, discovering better technologies in order to more effectively recognize and diagnose such impairments is crucial for the treatment of these. The research currently being conducted is exploring a machine learning approach stemming from Automatic Speech Recognition research to recognize early signs of speech impairments such as dyslexia and autism. The goal of the research and application being made is to recognize the early signs of such impairments in children so that they may seek the appropriate intervention from speech therapists, leading to better results from the therapy and overall better outcomes for the children.

After having completed UROP work with this lab for the past year and a half, I am excited to participate in a new project as a SuperUROP scholar. Throughout high school, I worked with preschool-aged children and earned certification in early childhood education. I am excited to incorporate the knowledge I have acquired about speech through past work and my expertise in early childhood into research that will benefit children and their development.