Research Project Title:
Bayesian Conditioning on Gaussian Mixture Models for Automatic Speech Signal Analysis
abstract:Current automatic speech-recognition system approaches tend not to use information about speech production when analyzing speech signals. This project focuses on making improvements to an automatic speech-recognition system that uses acoustic cues and speech signal features to generate the words spoken from a signal. The goal of my project is to improve the system’s accuracy by finding ways to integrate new information into the system’s model. One way of achieving that is by using formant frequencies to infer different properties about the speaker and using Bayesian conditioning to update the probability distributions used by the model.
“I am participating in SuperUROP because I would like to gain more research experience and apply what I have learned from previous research to the current project. I hope to learn more about acoustic signal analysis and how to apply this knowledge to the project. I am very excited to learn more about the applications of machine learning to natural languages and signals.”