Holbert, Sarah O.

Background/Objectives: Speech perception is measured in quiet and in the presence of background noise, such as multi-talker babble (MTB), to establish cochlear implant (CI) candidacy and measure post-operative outcomes. Testing protocols often use interrupted noise that begins just before and ends shortly after the target sentence, providing insufficient time for the noise-reduction algorithms to activate in the hearing device and provide an improved signal-to-noise ratio (SNR). Furthermore, interrupted noise does not allow the hearing-impaired user to acclimatize to the abrupt onset of noise or to focus on the target sentence, a challenge related to auditory stream segregation. The present study compares speech perception in bimodal CI users across interrupted and continuous MTB noise conditions to highlight performance differences between these two distinct listening environments. Methods: Speech perception was evaluated for two different bimodal CI groups. Group 1: This group evaluated an automatic hearing aid program across three environments: quiet, continuous MTB, and interrupted MTB. Group 2: This group evaluated two manual hearing aid programs across the same three environments (quiet, continuous, and interrupted MTB). Results: For Group 1, scores in quiet reached 88%, significantly declined in interrupted MTB (38%), but improved in continuous MTB (67%). For Group 2, no significant mean differences were found between interrupted and continuous noise across the two manual programs, though individual variations persisted. Conclusions: Based on the findings of this study, continuous noise rather than interrupted noise should be utilized for establishing CI candidacy and evaluating post-implantation speech perception.

Background/Objectives: Traditional hearing aid noise reduction algorithms offer no additional benefit in noisy situations for bimodal cochlear implant (CI) users with a CI in one ear and a hearing aid (HA) in the other. Recent breakthroughs in deep neural network (DNN)-based noise reduction have improved speech understanding for hearing aid users in noisy environments. These advancements could also boost speech perception in noise for bimodal CI users. This study investigated the effectiveness of DNN-based noise reduction in the HAs used by bimodal CI patients. Methods: Eleven bimodal CI patients, aged 71–89 years old, were fit with a Phonak Audéo Sphere Infinio 90 HA in their non-implanted ear and were provided with a Calm Situation program and Spheric Speech in Loud Noise program that uses DNN-based noise reduction. Sentence recognition scores were measured using AzBio sentences in quiet and in noise with the CI alone, hearing aid alone, and bimodally with both the Calm Situation and DNN HA programs. Results: The DNN program in the hearing aid significantly improved bimodal performance in noise, with sentence recognition scores reaching 79% compared to 60% with Calm Situation (a 19% average benefit, p < 0.001). When compared to the CI-alone condition in multi-talker babble, the DNN HA program offered a 40% bimodal benefit, significantly higher than the 21% score seen with the Calm Situation program. Conclusions: DNN-based noise reduction in HA significantly improves speech understanding in noise for bimodal CI users. Utilization of this technology is a promising option to address patients’ common complaint of speech understanding in noise.