Speech Enhancement in Noisy Environments in Hearing Aids Driven by a Tailored Gain Function Based on a Gaussian Mixture Model

Abstract

This paper centers on a novel approach aiming at speech enhancement in hearing aids. It consists in creating -by making use of perceptual concepts, and a supervised learning process driven by a genetic algorithm (GA)- a gain function ($\mathcal{G}$) that not only does it enhance the speech quality but also the speech intelligibility in noisy environments. The proposed algorithm creates the enhanced gain function by using a Gaussian mixture model fueled by the GA. To what extent the speech quality is enhanced is quantitatively measured by the algorithm itself by using a scheme based on the perceptual evaluation of speech quality (PESQ) standard. In this “blind” process, it does not use any initial information but that iteratively quantified by the PESQ measurement. The GA computes the optimized parameters that maximize the PESQ score. The experimental work, carried out over three different databases, shows how the computed gain function assists the hearing aid in enhancing speech, when compared to the values reached by using a standard hearing aid based on a multiband compressor-expander algorithm.