Evaluation of Signal Processing Methods for Speech Enhancement
Mahika Dubey · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2016
This thesis explores some of the main approaches to the problem of speech signal enhancement. Traditional signal processing techniques including spectral subtraction, Wiener filtering, and subspace methods are very widely used and can produce very good results, especially in the cases of constant ambient noise, or noise that is predictable over the course of the signal. We first study these methods and their results, and conclude with an analysis of the successes and failures of each. Comparisons are based on the effectiveness of the methods of removing disruptive noise, the speech quality and intelligibility of the enhanced signals, and whether or not they introduce some new artifacts into the signal. These characteristics are analyzed using the perceptual evaluation of speech quality (PESQ) measure, the segmental signal-to-noise ratio (SNR), the log likelihood ratio (LLR), and weighted spectral slope distance.