# 21: Comparison of Neural and Fuzzy Models for Vowel Recognition
Project ID:
21
Team Members:
Atul Shrotriya, Shilpa More
Faculty Advisor(s):
Frank Lewis
Department:
Electrical Engineering
Project Type:
Graduate
Title:
Comparison of Neural and Fuzzy Models for Vowel Recognition
Abstract:
Speech recognition has long been used and implemented in controlled environments. It is quickly becoming mainstream with the advancements in deep learning techniques. In this research, different basic neural and fuzzy logic models were compared to determine the best suited techniques for recognizing vowels as they are at the core of speech processing in many methodologies. With conventional equipment used for recording in an uncontrolled environment, this project defines the challenges which can be faced in developing reliable and robust models without using large amounts of pre-recorded data