Leveraging spatial cues from cochlear implant microphones to efficiently enhance speech separation in naturalistic listening scenes.

ABSTRACT: Despite the success of speech separation approaches for dry (non-reverb) speech mixtures, speech separation in naturalistic, spatial, and reverberant acoustic environments remains challenging. This limits the effectiveness of current speech separation methods for assistive hearing devices as well as neuroprosthetic devices such as cochlear implants (CIs). Here, we investigate whether a deep neural network model for speech separation can utilize the spatial information in naturalistic listening scenes as captured by a CI's microphones to improve separation performance. We examined the impact of latent spatial cues (inherently present in two-channel speech mixtures, but need to be learned from these mixtures), as well as pre-computed spatial cues added to the speech mixtures as auxiliary inp

SUBMITTER: Olalere F 

PROVIDER: S-EPMC12815942 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature