Purpose The aim of this study was to develop and evaluate a novel, automated speech-in-noise test viable for widespread in situ and remote screening. Method Vowel–consonant–vowel sounds in a multiple-choice consonant discrimination task were used. Recordings from a professional male native English speaker were used. A novel adaptive staircase procedure was developed, based on the estimated intelligibility of stimuli rather than on theoretical binomial models. Test performance was assessed in a population of 26 young adults (YAs) with normal hearing and in 72 unscreened adults (UAs), including native and nonnative English listeners. Results The proposed test provided accurate estimates of the speech recognition threshold (SRT) compared to a conventional adaptive procedure. Consistent outcomes were observed in YAs in test/retest and in controlled/uncontrolled conditions and in UAs in native and nonnative listeners. The SRT increased with increasing age, hearing loss, and self-reported hearing handicap in UAs. Test duration was similar in YAs and UAs irrespective of age and hearing loss. The test–retest repeatability of SRTs was high (Pearson correlation coefficient = .84), and the pass/fail outcomes of the test were reliable in repeated measures (Cohen's κ = .8). The test was accurate in identifying ears with pure-tone thresholds > 25 dB HL (accuracy = 0.82). Conclusion This study demonstrated the viability of the proposed test in subjects of varying language in terms of accuracy, reliability, and short test time. Further research is needed to validate the test in a larger population across a wider range of languages and hearing loss and to identify optimal classification criteria for screening purposes.

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Purpose: The aim of this study was to analyze the performance of multivariate machine learning (ML) models applied to a speech-in-noise hearing screening test and investigate the contribution of the measured features toward hearing loss detection using explainability techniques. Method: Seven different ML techniques, including transparent (i.e., decision tree and logistic regression) and opaque (e.g., random forest) models, were trained and evaluated on a data set including 215 tested ears (99 with hearing loss of mild degree or higher and 116 with no hearing loss). Post hoc explainability techniques were applied to highlight the role of each feature in predicting hearing loss. Results: Random forest (accuracy = .85, sensitivity = .86, specificity = .85, precision = .84) performed, on average, better than decision tree (accuracy = .82, sensitivity = .84, specificity = .80, precision = .79). Support vector machine, logistic regression, and gradient boosting had similar performance as random forest. According to post hoc explainability analysis on models generated using random forest, the features with the highest relevance in predicting hearing loss were age, number and percentage of correct responses, and average reaction time, whereas the total test time had the lowest relevance. Conclusions: This study demonstrates that a multivariate approach can help detect hearing loss with satisfactory performance. Further research on a bigger sample and using more complex ML algorithms and explainability techniques is needed to fully investigate the role of input features (including additional features such as risk factors and individual responses to low-/high-frequency stimuli) in predicting hearing loss.