Improving Speech Intelligibility in Binaural Hearing Aids by Estimating a Time-Frequency Mask with a Weighted Least Squares Classifier
Abstract
An efficient algorithm for speech enhancement in binaural hearing aids is proposed. The algorithm is based on the estimation of a time-frequency mask using supervised machine learning. The standard least-squares linear classifier is reformulated to optimize a metric related to speech/noise separation. The method is energy-efficient in two ways: the computational complexity is limited and the wireless data transmission optimized. The ability of the algorithm to enhance speech contaminated with different types of noise and low SNR has been evaluated. Objective measures of speech intelligibility and speech quality demonstrate that the algorithm increments both the hearing comfort and speech understanding of the user. These results are supported by subjective listening tests.