Evaluation of Speech Enhancement in Noisy Conditions Using a Spectral Subtraction and Linear Prediction Combination

Improving quality and intelligibility of speech signals in mobile devices has been studied with great interest in the past. Speech information in communication channels is usually corrupted by additive acoustic noise, reverberation or channel noise. This paper explores into the possibilities of enhancing corrupted speech using the Spectral Subtraction (SS) and Linear Prediction Coding (LPC) system for mobile applications only in acoustically noisy conditions. A Spectral Subtraction block is cascaded in series with a LPC system. For a pth order LPC system, a Levinson-Durbin based algorithm computes the LPC coefficients. Typically LPC is used as a data reduction system in speech communication but in this work, we try to find an optimum pth order LPC system that could enhance speech quality. We focus on improving speech quality and not speech intelligibility in this paper. The algorithm output will be evaluated objectively with a combined Perceptual Evaluation of Speech Quality (PESQ) and Itakura-Saito (IS) system and will be compared against Mean Opinion Scores (MOS) of various other Speech Enhancement algorithms.