Hearing loss is one of the most common disorders worldwide. It affects communicative abilities in all age groups. However, it is well known that elderly people suffer more frequently from hearing loss. Two different model approaches were employed: A generalised linear model and a random forest regression model were used to quantify the relationship between pure-tone hearing loss, age, and speech perception. Both models were applied to a large clinical data set of 19,801 ears, covering all degrees of hearing loss. They allow the estimation of age-related decline in speech recognition for different types of audiograms. Our results show that speech scores depend on the specific type of hearing loss and life decade. We found age effects for all degrees of hearing loss. A deterioration in speech recognition of up to 25 percentage points across the whole life span was observed for constant pure-tone thresholds. The largest decrease was 10 percentage points per life decade. This age-related decline in speech recognition cannot be explained by elevated hearing thresholds as measured by pure-tone audiometry.
Keywords: age-related hearing loss (ARHL); hearing loss; machine learning; maximum word recognition; random forest regression; speech audiometry; speech perception.
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