Hogan, Cynthia A.

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: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation. Methods: Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. Results: The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4–5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study. Conclusions: DNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely.