Introducing Undergraduates to Pattern Recognition and Machine Learning through Speech Processing

Abstract

This paper describes an educational project experience that achieves a software implementation and performance analysis of a blind signal to noise ratio (SNR) estimation system for noisy speech. The system is based on a pattern recognition paradigm and no clean speech reference signal is available. It is a product of the faculty’s research and funded by a government contract. The faculty’s research on a real-world issue in speech processing has been converted into an undergraduate project. Assessment results show that the project is viewed very favorably by students. Target versus control group results show that the target group feels better qualified for graduate study and career options in digital signal processing.