The study results supported the hypotheses. OSNR was found to have an accuracy equivalent to or better than ISNR, STOI, and VSTOI for tests conducted at a fixed presentation level and variable ISNR. OSNR was a more accurate metric than VSTOI for tests with fixed ISNRs and variable presentation levels. Overall, OSNR was the most accurate metric across the three data sets. OSNR holds promise as a prediction metric which could potentially improve the effectiveness of sound processor research and CI fitting.

Disability is an important and often overlooked component of diversity. Individuals with disabilities bring a rare perspective to science, technology, engineering, mathematics, and medicine (STEMM) because of their unique experiences approaching complex issues related to health and disability, navigating the healthcare system, creatively solving problems unfamiliar to many individuals without disabilities, managing time and resources that are limited by physical or mental constraints, and advocating for themselves and others in the disabled community. Yet, individuals with disabilities are underrepresented in STEMM. Professional organizations can address this underrepresentation by recruiting individuals with disabilities for leadership opportunities, easing financial burdens, providing equal access, fostering peer-mentor groups, and establishing a culture of equity and inclusion spanning all facets of diversity. We are a group of deaf and hard-of-hearing (D/HH) engineers, scientists, and clinicians, most of whom are active in clinical practice and/or auditory research. We have worked within our professional societies to improve access and inclusion for D/HH individuals and others with disabilities. We describe how different models of disability inform our understanding of disability as a form of diversity. We address heterogeneity within disabled communities, including intersectionality between disability and other forms of diversity. We highlight how the Association for Research in Otolaryngology has supported our efforts to reduce ableism and promote access and inclusion for D/HH individuals. We also discuss future directions and challenges. The tools and approaches discussed here can be applied by other professional organizations to include individuals with all forms of diversity in STEMM.

Objectives: A cochlear implant (CI) implements a variety of sound processing algorithms that seek to improve speech intelligibility. Typically, only a small number of parameter combinations are evaluated with recipients but the optimal configuration may differ for individuals. The present study evaluates a novel methodology which uses the output signal to noise ratio (OSNR) to predict complete psychometric functions that relate speech recognition to signal to noise ratio for individual CI recipients. Design: Speech scores from sentence-in-noise tests in a “reference” condition were mapped to OSNR and a psychometric function was fitted. The reference variability was defined as the root mean square error between the reference scores and the fitted curve. To predict individual scores in a different condition, OSNRs in that condition were calculated and the corresponding scores were read from the reference psychometric function. In a retrospective experiment, scores were predicted for each condition and subject in three existing data sets of sentence scores. The prediction error was defined as the root mean square error between observed and predicted scores. In data set 1, sentences were mixed with 20 talker babble or speech weighted noise and presented at 65 dB sound pressure level (SPL). An adaptive test procedure was used. Sound processing was advanced combinatorial encoding (ACE, Cochlear Limited) and ACE with ideal binary mask processing, with five different threshold settings. In data set 2, sentences were mixed with speech weighted noise, street-side city noise or cocktail party noise and presented at 65 dB SPL. An adaptive test procedure was used. Sound processing was ACE and ACE with two different noise reduction schemes. In data set 3, sentences were mixed with four-talker babble at two input SNRs and presented at levels of 55–89 dB SPL. Sound processing utilised three different automatic gain control configurations. Results: For data set 1, the median of individual prediction errors across all subjects, noise types and conditions, was 12% points, slightly better than the reference variability. The OSNR prediction method was inaccurate for the specific condition with a gain threshold of +10 dB. For data set 2, the median of individual prediction errors was 17% points and the reference variability was 11% points. For data set 3, the median prediction error was 9% points and the reference variability was 7% points. A Monte Carlo simulation found that the OSNR prediction method, which used reference scores and OSNR to predict individual scores in other conditions, was significantly more accurate (p < 0.01) than simply using reference scores as predictors. Conclusions: The results supported the hypothesis that the OSNR prediction method could accurately predict individual recipient scores for a range of algorithms and noise types, for all but one condition. The medians of the individual prediction errors for each data set were accurate within 6% points of the reference variability and compared favourably with prediction methodologies in other recent studies. Overall, the novel OSNR-based prediction method shows promise as a tool to assist researchers and clinicians in the development or fitting of CI sound processors.

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