SPEECH UNDERSTANDING IN NOISE PROVIDED BY A SIMULATED COCHLEAR IMPLANT PROCESSOR BASED ON MATCHING PURSUIT

Speech reception is poor for cochlear implant recipients in listening environments with interfering noise. This study investigates the speech understanding provided in interfering noise by a coding strategy based on the sparse approximation algorithm matching pursuit (MP) and additionally proposes two modifications to the strategy. The levels of spectral information provided by the MP strategy and the modified MP strategy are compared to that of continuous interleaved sampling (CIS) and a strategy based on the ideal binary mask (IBM) using vocoded speech and the normalized covariance metric (NCM). We demonstrate objective intelligibility improvements in quiet, and total and partial objective intelligibility restoration in steady-state and fluctuating noise, respectively.