Supporting Equity and Inclusion of Deaf and Hard-of-Hearing Individuals in Professional Organizations.

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.

Prediction of Individual Cochlear Implant Recipient Speech Perception With the Output Signal to Noise Ratio Metric.

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.

An Investigation of Audibility Effects on Cochlear Implant Speech Perception Prediction.

Output Signal to Noise Ratio (OSNR) is the Signal to Noise Ratio (SNR) at the output of a cochlear implant (CI) sound processor. Whereas other prediction metrics typically predict mean speech-in-noise test scores for a group of subjects, an OSNR-based model has been shown to accurately predict scores for individual CI recipients. The OSNR model was unable to predict scores for aggressive Ideal Binary Mask (IdBM) sound processing. This algorithm calculated Input Signal to Noise Ratio (ISNR), in each CI channel, and applied a gain function to suppress noise when a gain threshold was exceeded.The current study investigated the effect of IdBM processing on the separate speech and noise signals to determine whether audibility was affecting intelligibility. A novel metric, "OSNR and Power" (OSNRP), which combined the effect of the reduction in output speech power with OSNR, was proposed.It was found that the IdBM reduced the output speech level, likely causing audibility issues, at poor ISNRs. OSNRP accurately predicted individual speech-in-noise test scores for aggressive IdBM.The novel OSNRP metric has potential as a tool for calculating optimum configurations for sound processor parameter settings for individual CI recipients. We propose using a prescribed set of reference test conditions, the results of which can be utilized to predict outcomes when using alternative sound processing parameters and techniques, and to tailor them to the individual needs of individual CI recipients.

An Investigation of the Effect of AGC Gain on the Output Signal to Noise Ratio in Cochlear Implant Sound Processing.

Measurement of speech intelligibility of cochlear implant (CI) recipients is typically carried out with a speech-innoise test procedure. Metrics which predict speech intelligibility can pre-screen new sound processing strategies prior to comprehensive testing with human subjects.The Output Signal to Noise Ratio (OSNR) metric calculates the Signal to Noise Ratio (SNR) which is present at the CI sound processor output. Watkins et al. (2018) found OSNR was an accurate predictor of speech intelligibility that could predict intelligibility in scenarios where other predictors could not.The current study investigated the effect of the sound processor automatic gain control (AGC) on OSNR and a simplified metric, Separate gain SNR (SSNR), which calculated the SNR at the CI output, assuming no interaction between the signal and noise in the sound processor. Prediction accuracy of OSNR was compared to that of Input SNR and SSNR.It was found that AGC-induced distortion and SNR degradation in speech gaps worsened OSNR. For scenarios with significant non-linear, time-varying processing, OSNR was the most accurate prediction metric. SSNR was found to be an inaccurate predictor.

An Evaluation of Output Signal to Noise Ratio as a Predictor of Cochlear Implant Speech Intelligibility.

OBJECTIVES: Cochlear implant (CI) sound processing strategies are usually evaluated in clinical studies involving experienced implant recipients. Metrics which estimate the capacity to perceive speech for a given set of audio and processing conditions provide an alternative means to assess the effectiveness of processing strategies. The aim of this research was to assess the ability of the output signal to noise ratio (OSNR) to accurately predict speech perception. It was hypothesized that compared with the other metrics evaluated in this study (1) OSNR would have equivalent or better accuracy and (2) OSNR would be the most accurate in the presence of variable levels of speech presentation. DESIGN: For the first time, the accuracy of OSNR as a metric which predicts speech intelligibility was compared, in a retrospective study, with that of the input signal to noise ratio (ISNR) and the short-term objective intelligibility (STOI) metric. Because STOI measured audio quality at the input to a CI sound processor, a vocoder was applied to the sound processor output and STOI was also calculated for the reconstructed audio signal (vocoder short-term objective intelligibility [VSTOI] metric). The figures of merit calculated for each metric were Pearson correlation of the metric and a psychometric function fitted to sentence scores at each predictor value (Pearson sigmoidal correlation [PSIG]), epsilon insensitive root mean square error (RMSE*) of the psychometric function and the sentence scores, and the statistical deviance of the fitted curve to the sentence scores (D). Sentence scores were taken from three existing data sets of Australian Sentence Tests in Noise results. The AuSTIN tests were conducted with experienced users of the Nucleus CI system. The score for each sentence was the proportion of morphemes the participant correctly repeated. In data set 1, all sentences were presented at 65 dB sound pressure level (SPL) in the presence of four-talker Babble noise. Each block of sentences used an adaptive procedure, with the speech presented at a fixed level and the ISNR varied. In data set 2, sentences were presented at 65 dB SPL in the presence of stationary speech weighted noise, street-side city noise, and cocktail party noise. An adaptive ISNR procedure was used. In data set 3, sentences were presented at levels ranging from 55 to 89 dB SPL with two automatic gain control configurations and two fixed ISNRs. RESULTS: For data set 1, the ISNR and OSNR were equally most accurate. STOI was significantly different for deviance (p = 0.045) and RMSE* (p < 0.001). VSTOI was significantly different for RMSE* (p < 0.001). For data set 2, ISNR and OSNR had an equivalent accuracy which was significantly better than that of STOI for PSIG (p = 0.029) and VSTOI for deviance (p = 0.001), RMSE*, and PSIG (both p < 0.001). For data set 3, OSNR was the most accurate metric and was significantly more accurate than VSTOI for deviance, RMSE*, and PSIG (all p < 0.001). ISNR and STOI were unable to predict the sentence scores for this data set. CONCLUSIONS: 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.

A simulation analysis of the variability of the roving level hearing test.

In the study of auditory prostheses, the Speech Recognition Threshold (SRT) is the Signal to Noise Ratio (SNR) at which 50% of words are correctly identified. SRT is typically measured using an adaptive procedure wherein speech is presented at a fixed sound pressure level (SPL) and the noise level is varied according to the subject's responses. A roving level SRT test has been used by researchers with the goal of including the effectiveness of Automatic Gain Control (AGC) systems in SRT measurements. The roving method presents speech at three different SPLs with the level for each sentence chosen pseudo-randomly, while adaptively varying the SNR. This study used simulations to compare roving and fixed level SRT tests. It was found that roving level tests have significantly increased variability when there are level-dependent differences in subject scores. The interleaved level test is recommended as an alternative as it provides clear visibility of level-dependent performance and a better understanding of overall subject performance.