Data-Driven Prediction of Speech-in-Noise Performance: A Systematic Review and Meta-analysis of Machine Learning Models.

purposeDifficulty understanding speech in noise (SIN) is a major determinant of communication ability and quality of life, affecting individuals with hearing loss as well as some with normal hearing. Data-driven prediction methods offer a promising approach for modeling complex, multidimensional factors underlying speech perception. This systematic review and meta-analysis evaluated computational approaches for predicting SIN-related outcomes, compared predictive performance across model classes where quantitative synthesis was feasible, and identified methodological factors relevant to model selection, generalizability, and clinical translation.

methodsFollowing PRISMA 2020 guidelines, we conducted a systematic review and meta-analysis of studies using machine learning (ML), neural networks, automatic speech recognition (ASR), or auditory model-based approaches to predict, simulate, or evaluate SIN-related outcomes. Quantitative synthesis was performed using random-effects models for studies reporting support vector machine (SVM) or random forest (RF) performance, and between-study heterogeneity was assessed.

resultsEligible studies included traditional ML, neural network, ASR-based, and mechanistic auditory modeling approaches. SVM accuracy pooled at 0.71 and SVM area under the curve at 0.84, while RF accuracy pooled at 0.86 and RF recall at 0.81. Bootstrap estimates were broadly consistent with the primary random-effects estimates, although between-study heterogeneity ranged from moderate to very high across analyses.

conclusionData-driven prediction approaches show promise for predicting SIN-related outcomes, but high heterogeneity precludes identifying a single optimal model. Model selection should reflect the prediction target, data type, sample size, interpretability, and intended use. Current evidence supports proof-of-concept performance more strongly than clinical utility, highlighting the need for larger, standardized, externally validated studies.