Emerging Speech-in-Noise Tools for the Assessment of Hearing Loss: A Scoping Review

Background/Objectives: The objective of this scoping review was to map and critically describe emerging speech-in-noise assessment tools developed over the last decade for the evaluation of hearing loss beyond conventional audiological measures. Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive literature search was performed in the PubMed/MEDLINE, Scopus, and Embase databases. A comprehensive review of studies describing novel or emerging SIN-based assessment tools was conducted, with a particular emphasis on those including adult participants with normal hearing and hearing loss. Results: Nine studies met the inclusion criteria and were included in the review. The identified tools cover a range of methodological innovations, including advanced digits-in-noise paradigms, antiphasic and binaural presentation modes, optimized adaptive procedures, and digital or automated testing platforms. Several studies also incorporated artificial intelligence-based approaches, such as machine learning, text-to-speech, and automatic speech recognition, to enhance test development, administration, and hearing loss classification. Across all studies, SIN measures demonstrated the ability to reliably differentiate between normal hearing listeners and individuals with hearing loss and to provide complementary information beyond pure-tone audiometry. Conclusions: Emerging speech-in-noise tools show considerable potential to improve the functional assessment of hearing loss and to support more sensitive, accessible, and scalable approaches for hearing evaluation. Further research is required to assess their clinical integration and long-term impact on hearing screening and diagnostic pathways.