Date of Award

6-7-2017

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2017.

Department

Computer Engineering

First Advisor

Yi Fang

Abstract

We built a mobile application that improves speed and personalization in conversations for people struggling with verbal communication. Many people diagnosed with autism, Down syndrome, and other disorders face daily challenges involving communication due to speech impediments. Existing solutions allow users to communicate via speech cards or typing on a keyboard. However, these solutions make tradeoffs between personalization and speed, compromising what it takes to have fluid, natural) and rewarding conversations. Our solution speeds up personalized communication by applying Machine Learning principles, Artificial Intelligence, and Natural Language Processing. Our project will predict how a user will respond based on natural language processing results of verbal input and some base-layer artificial intelligence rules) allowing the user to communicate quickly with their own voice.

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