Effect of interaural electrode insertion depth difference and independent band selection on sentence recognition in noise and spatial release from masking in simulated bilateral cochlear implant listening

Abstract

Purpose Inter-aural insertion depth difference (IEDD) in bilateral cochlear implant (BiCI) with continuous interleaved sampling (CIS) processing is known to reduce the recognition of speech in noise and spatial release from masking (SRM). However, the independent channel selection in the ‘n-of-m’ sound coding strategy might have a different effect on speech recognition and SRM when compared to the effects of IEDD in CIS-based findings. This study aimed to investigate the effect of bilateral ‘n-of-m’ processing strategy and interaural electrode insertion depth difference on speech recognition in noise and SRM under conditions that simulated bilateral cochlear implant listening. Methods Five young adults with normal hearing sensitivity participated in the study. The target sentences were spatially filtered to originate from 0° and the masker was spatially filtered at 0°, 15°, 37.5°, and 90° using the Oldenburg head-related transfer function database for behind the ear microphone. A 22-channel sine wave vocoder processing based on ‘n-of-m’ processing was applied to the spatialized target-masker mixture, in each ear. The perceptual experiment involved a test of speech recognition in noise under one co-located condition (target and masker at 0°) and three spatially separated conditions (target at 0°, masker at 15°, 37.5°, or 90° to the right ear). Results The results were analyzed using a three-way repeated measure analysis of variance (ANOVA). The effect of interaural insertion depth difference (F (2,8) = 3.145, p = 0.098, ɳ2 = 0.007) and spatial separation between target and masker (F (3,12) = 1.239, p = 0.339, ɳ2 = 0.004) on speech recognition in noise was not significant. Conclusions Speech recognition in noise and SRM were not affected by IEDD ≤ 3 mm. Bilateral ‘n-of-m’ processing resulted in reduced speech recognition in noise and SRM.