Objective comparison of auditory profiles using manifold learning and intrinsic measures

Assigning individuals with hearing impairment to auditory profiles can support a better understanding of the causes and consequences of hearing loss and facilitate profile-based hearing-aid fitting. However, the factors influencing auditory profile generation remain insufficiently understood, and existing profiling frameworks have rarely been compared systematically. This study therefore investigated the impact of two key factors - the clustering method and the number of profiles - on auditory profile generation. In addition, eight established auditory profiling frameworks were systematically reviewed and compared using intrinsic statistical measures and manifold learning techniques. Frameworks were evaluated with respect to internal consistency (i.e., grouping similar individuals) and cluster separation (i.e., clear differentiation between groups). To ensure comparability, all analyses were conducted on a common open-access dataset, the extended Oldenburg Hearing Health Record (OHHR), comprising 1,127 participants (mean age = 67.2 years, SD = 12.0). Results showed that both the clustering method and the chosen number of profiles substantially influenced the resulting auditory profiles. Among purely audiogram-based approaches, the Bisgaard auditory profiles demonstrated the strongest clustering performance, whereas audiometric phenotypes performed worst. Among frameworks incorporating supra-threshold information in addition to the audiogram, the Hearing4All auditory profiles were advantageous, combining a near-optimal number of profile classes (N = 13) with high clustering quality, as indicated by a low Davies-Bouldin index. In conclusion, manifold learning and intrinsic measures enable systematic comparison of auditory profiling frameworks and identify the Hearing4All auditory profile as a promising approach for future research.

Discrimination loss vs. SRT: A model-based approach towards harmonizing speech test interpretations

Objective: Speech tests aim to estimate discrimination loss or speech recognition threshold (SRT). This paper investigates the potential to estimate SRTs from clinical data that target at characterizing the discrimination loss. Knowledge about the relationship between the speech test outcome variables--conceptually linked via the psychometric function--is important towards integration of data from different databases. Design: Depending on the available data, different SRT estimation procedures were compared and evaluated. A novel, model-based SRT estimation procedure was proposed that deals with incomplete patient data. Interpretations of supra-threshold deficits were assessed for the two interpretation modes. Study sample: Data for 27009 patients with Freiburg monosyllabic speech test (FMST) and audiogram (AG) results from the same day were included in the retrospective analysis. Results: The model-based SRT estimation procedure provided accurate SRTs, but with large deviations in the estimated slope. Supra-threshold hearing loss components differed between the two interpretation modes. Conclusions: The model-based procedure can be used for SRT estimation, and its properties relate to data availability for individual patients. All SRT procedures are influenced by the uncertainty of the word recognition scores. In the future, the proposed approach can be used to assess additional differences between speech tests.